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NEWS AND VIEWS
23 June 2021

Migratory birds aid the redistribution of plants to new climates

Birds that travel long distances can disperse seeds far and wide. An assessment of the timing and direction of European bird migration reveals how these patterns might affect seed dispersal as the planet warms.

Barnabas H. Daru

 ORCID: http://orcid.org/0000-0002-2115-0257

0

Barnabas H. Daru

Barnabas H. Daru is in the Department of Life Sciences, Texas A&M University-Corpus Christi, Corpus Christi, Texas 78412, USA.

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The rapid pace of global warming and its effects on habitats raise the question of whether species are able to keep up so that they remain in suitable living conditions. Some animals can move fast to adjust to a swiftly changing climate. Plants, being less mobile, rely on means such as seed dispersal by animals, wind or water to move to new areas, but this redistribution typically occurs within one kilometre of the original plant1. Writing in Nature, González-Varo et al.2 shed light on the potential capacity of migratory birds to aid seed dispersal.When the climate in a plant’s usual range becomes hotter than it can tolerate, it must colonize new, cooler areas that might lie many kilometres away. It is not fully clear how plants distribute their seeds across great distances, let alone how they cross geographical barriers. One explanation for long-distance seed dispersal is through transport by migratory birds. Such birds ingest viable seeds when eating fruit (Fig. 1) and can move them tens or hundreds of kilometres outside the range of a plant species3. In this mode of dispersal, the seeds pass through the bird’s digestive tract unharmed4,5 and are deposited in faeces, which provides fertilizer that aids plant growth. In the case of European migratory birds, for example, the direction of seed dispersal will depend on whether the timing of fruit production coincides with a bird’s southward trip to warmer regions around the Equator, or northward to cooler regions. Many aspects of this process have been a mystery until now.

Figure 1 | A young blackcap bird (Sylvia atricapilla) eating elderberries.Credit: Getty

González-Varo and colleagues report how plants might be able to keep pace with rapid climate change through the help of migrating birds. The authors analysed the fruiting times of plants, patterns of bird migration and the interactions between fruit-eating birds and fleshy-fruited plants across Europe. Plants with fleshy fruits were chosen for this study because most of their seed transport is by migratory birds6, and because fleshy-fruited plants are an important component of the woody-plant community in Europe. The common approach until now has been to predict plant dispersal and colonization using models fitted to abiotic factors, such as the current climate. González-Varo et al. instead analysed an impressive data set of 949 different seed-dispersal interactions between bird and plant communities, together with data on entire fruiting times and migratory patterns of birds across Europe. The researchers also analysed DNA traces from bird faeces to identify the plants and birds responsible for seed dispersal.
Read the paper: Limited potential for bird migration to disperse plants to cooler latitudes
The authors hypothesized that the direction of seed migration depends on how the plants interact with migratory birds, the frequency of these interactions or the number of bird species that might transport seeds from each plant species. González-Varo and colleagues found that 86% of plant species studied might have seeds dispersed by birds during their southward trip towards drier and hotter equatorial regions in autumn, whereas only about one-third of the plant species might be dispersed by birds migrating north in spring. This dispersal trend was more pronounced in temperate plants than in the Mediterranean plant communities examined. These results are in general agreement with well-known patterns of fruiting times and bird migrations. For example, the fruit of most fleshy-fruited plants in Europe ripens at a time that coincides with when birds migrate south towards the Equator7.Perhaps the most striking feature of these inferred seed movements is the observation that 35% of plant species across European communities, which are closely related on the evolutionary tree (phylogenetically related), might benefit from long-distance dispersal by the northward journey of migratory birds. This particular subset of plants tends to fruit over a long period of time, or has fruits that persist over the winter. This means that the ability of plants to keep up with climate change could be shaped by their evolutionary history — implying that future plant communities in the Northern Hemisphere will probably come from plant species that are phylogenetically closely related and that have migrated from the south. Or, to put it another way, the overwhelming majority of plant species that are dispersed south towards drier and hotter regions at the Equator will probably be less able to keep pace with rapid climate change in their new locations than will the few ‘winners’ that are instead dispersed north to cooler climates. This has implications for understanding how plants will respond to climate change, and for assessing ecosystem functions and community assembly at higher levels of the food chain. However, for seeds of a given plant species, more evidence is needed to assess whether passing through the guts of birds affects germination success.To determine which birds might be responsible for the plant redistributions to cooler climates in the north, the authors categorized European bird migrants into Palaearctic (those that fly to southern Europe and northern Africa during their non-breeding season) and Afro-Palaearctic (those that winter in sub-Saharan Africa). Only a few common Palaearctic migrants, such as the blackcap (Sylvia atricapilla; Fig. 1) or blackbird (Turdus merula), provide most of this crucial dispersal service northwards to cooler regions across Europe. Because migratory birds are able to relocate a small, non-random subset of plants, this could well have a strong influence on the types of plant community that will form under climate-change conditions.
A bird’s migration decoded
A major problem, however, is that the role of these birds in dispersing seeds over long distances is already at risk from human pressures and environmental changes8. Understanding these large-scale seed-dispersal interactions offers a way for targeted conservation actions to protect the areas that are most vulnerable to climate change. This could include boosting protection efforts in and around the wintering grounds of migratory birds — locations that are already experiencing a rise in human pressures, such as illegal bird hunting.González-Varo and colleagues’ focus on seed dispersal across a Northern Hemisphere region means that, as with most ecological analyses, the results are dependent on scale, which can cause issues when interpreting data9. Because the Northern Hemisphere has more land area and steeper seasonal temperature gradients than the Southern Hemisphere does, seed-dispersal interactions might have different patterns from those occurring in the Southern Hemisphere or in aquatic systems.For example, seed-eating birds from the genus Quelea migrate from the Southern Hemisphere to spend the dry season in equatorial West Africa, then move southwards again when the rains arrive. Their arrival in southern Africa usually coincides with the end of the wet season in this region, when annual grass seeds are in abundance. It will be worth investigating whether migratory birds in the Southern Hemisphere also influence the redistribution of plant communities during global warming. Likewise, exploring the long-distance dispersal of seeds of aquatic plants, such as seagrasses10 by water birds, is another area for future research that might benefit from González-Varo and colleagues’ methods.This study provides a great example of how migratory birds might assist plant redistribution to new locations that would normally be difficult for them to reach on their own, and which might offer a suitable climate. As the planet warms, understanding how such biological mechanisms reorganize plant communities complements the information available from climate-projection models, which offer predictions of future species distributions.

doi: https://doi.org/10.1038/d41586-021-01547-1

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Competing Interests
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Baseline model: no competitionWe start by assuming no competition and consider unconstrained growth in each of the two compartments. In this case, the equations describing our model become linear and can be rewritten in matrix form [4] as$$left( {begin{array}{*{20}{c}} {frac{{partial n_{H}}}{{partial t}}} \ {frac{{partial n_{E}}}{{partial t}}} end{array}} right) = underbrace{left( {begin{array}{*{20}{c}} {r_{H} – m_{E}} & {m_{H}} \ {m_{E}} & {r_{E} – m_{H}} end{array}} right)}_{{mathrm{projection}}, {mathrm{matrix}}}left( {begin{array}{*{20}{c}} {n_{H}} \ {n_{E}}end{array}} right)$$
(2)
The dominant eigenvalue λ of the above-defined projection matrix gives the asymptotic overall growth rate of the considered microbial lineage. This quantity is an appropriate measure of fitness [4] insofar as it measures reproductive as well as transmission success and recapitulates the effects of all the life-history traits (rE, rH, mE, and mH, also defining the phenotype in our model). Overall microbial fitness is thus integrated across the different steps of the life cycle, thereby considering the reproductive rates (i.e., replication rates) within each of the compartments and importantly transmission rates (i.e., migration rates) across the compartments. The dominant right eigenvector represents the stable distribution of microbes in the two compartments, and the number of microbes in each of the compartments grows exponentially with rate λ. The value of λ can be calculated at each point of the phenotypic space defined by the ranges of possible values that could be taken by the life-history traits rE, rH, mE, and mH. The dependence of λ on these traits tells us at which points of the phenotypic space fitness is maximized and how it can be increased at all other points.From the projection matrix, we calculate the dominant eigenvalue as$$lambda = frac{1}{2}left(sqrt {left( {r_E + r_H – m_E – m_H} right)^2 {,}-{,} 4left( {r_Er_H – r_Em_E – r_Hm_H} right)} + r_E +r_H – m_E – m_H right).$$
(3)
Note that if microbes replicate at the same rate in the host and in the environment, i.e., if rE = rH = r, λ simplifies to r, regardless of the migration rates mH and mE. When there is an asymmetry between the two replication rates however, which is very likely to be the case in nature, then the migration rates also affect the overall growth rate. In the following sections, we study this effect compared to the effect of the replication rates. We arbitrarily set rH ≤ rE, and rE  > 0 – otherwise the lineage goes extinct. In biological terms, this corresponds to the situation where the microbial lineage is initially more adapted to the environment than to the host and thus grows faster in the environment. But mathematically, in this model, host and environment are symmetrical, i.e., they only differ by the rates defined above. Thus, the chosen direction of this inequality does not carry any strong meaning, and there is no loss of generality in making this choice. In particular, one can access the opposite biological situation where microbes replicate faster in the host than in the environment – as is the case for viruses, that can only replicate in the host (rH  > 0) but decay in the environment (rE  0. Setting rE = 1 to scale time (and thus, measuring all other rates in units of the replication rate of the microbe in the environment), λ reduces to$${uplambda}_{sym} = frac{1}{2}left( {1 + r_H – 2m + sqrt {left( {1 – r_H} right)^2 {,}+ {,}4m^2} } right)$$
(4)
For any fixed positive value of m, λsym is a strictly increasing function of rH, which reflects the fact that increasing rH allows for additional growth within the host. We will limit ourselves to the study of rH ≥ −1, which ensures a positive value for λsym. For any fixed value of rH, λsym is a decreasing function of m, which reflects the fact that for increasing m, microbes are increasingly lost towards the host, where growth is slower than in the environment. Figure 1C shows the value of λsym on the reduced phenotypic space defined by rH and m. The maximum possible value for λ is 1 (in units of rE). This value is achieved either by increasing the ratio of replication rates between host and environment, so that the replication rates in both compartments are identical (strategy I), or by reducing migration between host and environment, and in particular, by reducing mH (strategy II). This second strategy allows microbes to spend a longer time in the environment on average. Note however, that this strategy is limited, since setting m to zero decouples the two compartments completely, in which case the microbial lineage is no longer subject to a multi-step life cycle.How strong is the selection on these traits? This question can be approached by inferring how strongly the overall growth rate depends on the traits we are considering. One standard approach to measure this is sensitivity analysis [4]. One defines the sensitivity of the overall growth rate λ achieved by the phenotype described by the vector x = (x1,…, xN) in the trait space to its ith life-history trait as$$s_{mathrm{i}}left( {mathbf{x}} right) = left. {frac{{partial {uplambda}}}{{partial {mathrm{x}}_{mathrm{i}}}}} right|_{mathbf{x}}$$
(5)
This quantity gives the change in the value of λ that results from a small increment of the trait i. It is a local property that can be calculated for each point ({mathbf{x}}) of the trait space. The vector of the sensitivities at point ({mathbf{x}}) gives the direction of the selection gradient on the fitness landscape. In other words, to achieve efficient phenotypic adaptation, the lineage should move in the trait space following the direction of this gradient.If the lineage can invest in phenotypic adaptation only by tuning one of its life-history traits at a time, then it should act upon the trait that has the largest (absolute) sensitivity at the current position of the lineage in the trait space. In our model, in all generic cases (i.e., when m  > 0), the largest sensitivity is always associated to the increase of the trait rE, the replication rate in the fast-growing compartment. However, we assume that the considered microbial lineage is initially fully adapted to the environment, so that it has reached its evolutionary limit, and we can essentially ignore the sensitivity to rE throughout the manuscript to focus on the sensitivity to the other traits. This reasoning allows to divide the trait space into regions of distinct optimal strategies, as shown in Fig. 1C. In the regime of high migration rates (i.e., when the switch between the compartments is so rapid that the microbial lineage is almost experiencing a habitat having average properties between the host and the environment), strategy I (increasing rH) becomes almost always optimal, except for small replication ratios, where there is almost no replication in the host. In summary, migration rates are important when replication in the host is slow compared to the environment, and when migration itself is slow. These conclusions remain qualitatively unchanged with asymmetric migration rates, although a third optimal strategy (increasing mE) appears for an intermediate region of the traits space when the asymmetry is important (see electronic Supplementary Material (ESM) section 1 and Supplementary Fig. S1).Model with global competition between all microbesIn the baseline model, there are no constraints on growth. In nature, however, microbes do face limits to their growth. Since the equations above are linear and can only give rise to exponential growth or exponential decay, they can only describe the microbial dynamics over a limited period of time. In order to account for saturation and competition during growth, we thus need to introduce non-linear terms to the equations (1). The study of this kind of systems often focus on long-term dynamics, yet it can be of high practical relevance to study the transient optimal strategies, as shorter timescales are often relevant in the real world – whether it be due to experimental constraints or to ecological disturbances and perturbations [20]. Since we are going to consider some out-of equilibrium dynamics, in particular in the section with competition limited to one of the compartments, and because we are also interested in transient properties, we will adopt a numerical approach based on the number of microbes [21, 22].In this section, we study the case of a microbial lineage constrained by global competition occurring at rate k = kHH = kEE = kEH = kHE. This situation could correspond to a host-associated microbe living in direct contact with an external environment, e.g., on the surface of an organism. Alternatively, what we call the “environment” in our model could represent another host compartment in direct contact with the other, like the gut lumen and the colonic crypts. In that case, microbes living in association with the host are in direct contact with those in the environment and can mutually impact each other’s growth. This is of particular relevance if microbes living in both compartments rely on and are limited by the same nutrients for growth.From the microbial abundances in the two compartments obtained by numerically solving the equations, one can build a proxy for the overall growth rate of the microbial lineage. To remain consistent with the previous section, we define$$varLambda left( {mathbf{x}} right) = frac{1}{{t_{max}}}log left( {frac{{n_Eleft( {t_{max}} right) + n_Hleft( {t_{max}} right)}}{{n_Eleft( 0 right) + n_Hleft( 0 right)}}} right)$$
(6)
i.e., the effective exponential growth rate of the microbial lineage over a chosen period of time [0, tmax]. Figure 2A provides a graphical explanation for the expression of Λ. There are indeed several fundamental differences between the effective exponential growth rate Λ in a non-linear system and the asymptotic growth rate λ in a linear system, the dominant eigenvalue of the projection matrix as defined in the baseline model. First, Λ provides a measure of growth for the whole lineage, but is not an asymptotic growth rate (as compared to λ in the baseline model): in the case of global saturation, replication stops when the carrying capacity is reached, and the asymptotic growth rate for the whole lineage would thus be zero. Therefore, the choice of the probing time tmax has an impact on Λ, as shown in Fig. 2A. Second, the choice of the exact form of Λ now implies biological assumptions on the selection pressure experienced by the microbial lineage: choosing the effective exponential growth rate over the whole lineage as we do implies that selection is acting on both compartments evenly. There may be some situations in which the microbes in one of the compartments only are artificially selected for (e.g., as part of the protocol of an evolution experiment). In such cases, it would make sense to define Λ as the effective exponential growth rate over just this compartment. This may lead to different conclusions, in particular at the transient scale. One must thus adapt Λ to the specifics of the modeled system. In addition, the choice of tmax itself has a biological meaning, and should in particular not exceed the time upon which the dynamics of the system are accurately described by the set of equations. This may also be determined by experimental times.Fig. 2: Optimal strategies in the model with global competition.A Temporal dynamics of the total number of microbes nE(t) + nH(t) for three different sets of traits values, differing only by their intensity of competition k = kHH = kEE = kEH = kHE. Other parameter values are: rH = 0.1, mE = mH = 0.5. The effective overall growth rate Λ is calculated numerically by taking the slope of the straight line that connects the abundances in t = 0 and in tmax, thus making Λ a quantity that strongly depends on tmax. B Change in the contour line delimiting the regions of optimality of the two optimal strategies (strategy I: increasing rH; strategy II: decreasing mH) with tmax, the time chosen to measure the final number of microbes, measured in units of 1/rE. Initially the microbes are equally distributed between the host and the environment. Supplementary Fig. S2 shows how this is modified with different initial conditions. Because in this model all the microbes are equally impacted by competition, with tmax large enough, one recovers the contour line of the baseline model calculated analytically (black line). Continuous lines: k = 0, i.e., no competition. Dashed lines: increasing values of k (competition intensity). C, D Change in the fitness landscape with tmax (panel C: tmax = 0.7 and panel D: tmax = 3). The colored lines show the contour delimiting the regions of optimality of strategies I and II for three different values of k, as shown on panel B. Black line: long-term limit of no competition from the base model.Full size imageWe now calculate the sensitivity of Λ in the direction of the trait i at the point x of the phenotypic space as$$S_i = frac{{varLambda left( {x_1,x_2, ldots ,x_{i – 1},x_i + delta x_i,x_{i + 1}, ldots ,x_N} right) – varLambda left( {x_1,x_2, ldots ,x_N} right)}}{{delta x_i}}$$
(7)
with δxi the discretization interval, and N the number of traits defining a phenotype x.For this numerical approach, additional choices need to be made. First, the trait space needs to be discretized. Then, to calculate Eq. (7), one needs to choose a set of initial conditions and a probing time at which to measure the microbial abundances, as exposed in detail for the linear case in [20]. Finally, we need to choose the discretization interval δxi. In the following, we always choose δxi sufficiently small for convergence, i.e., so that it does not significantly impact the numerical values of the sensitivities, and focus on the choices of the other parameters (probing time and initial conditions) and the influence of the competition intensity k. One strategy to explore the possible impact of initial conditions is to use “stage biased vectors” [20], i.e., extreme initial distributions of microbes across the two compartments. This corresponds to initial conditions where microbes either exist only in the host or only in the environment.In Fig. 2B, we show how the contour lines delimiting the two optimal strategies change with the final time tmax chosen to measure the overall growth rate and with the intensity of competition k, for a mixed initial condition (nE(0) = 0.5, nH(0) = 0.5), and Supplementary Fig. S2 shows how this is modified with stage biased vectors. In all cases, with sufficiently long tmax, the contours converge to the contour plot of the baseline model shown in the previous section. This is expected, since competition here affects all the microbes in the same way, so that the equilibrium distribution is the same as the asymptotic distribution of the baseline model (given by the dominant eigenvector). Mathematically, global competition can be seen as a modification of the baseline projection matrix by subtracting an identity matrix times a scalar depending on time. This does neither affect the eigenvectors nor the dependence of the dominant eigenvalue on the traits.In the case where all the microbes are initially in the environment (Supplementary Fig. S2A), there is no transient effect and whichever tmax is chosen, all the contour lines collapse to the limit of the baseline case. In the case where all the microbes are initially in the host (Supplementary Fig. S2B), a third optimal strategy transiently appears (increasing mE) and remains at long times around m = 0. In this unfavorable condition (m = 0 and an initially empty environment), increasing the microbial flux towards the environment becomes more important than limiting the flux of microbes leaving it (which is nonexistent when m = 0).Finally, we observe that the intensity of competition has only a small effect on the contours (Fig. 2B and S2B), but increasing k appears to slightly accelerate convergence to the baseline contour. By limiting growth in the host compartment – when it is initially relatively more populated than in the asymptotic distribution – competition facilitates the convergence to the baseline asymptotic distribution, where most of the microbes live in the environment.Model with competition within one of the compartments onlyIn this section we consider competition happening inside one of the compartments only (i.e., kEH = kHE = 0 and kEE ≠ 0 or kHH ≠ 0). We will start by considering competition in the host only (the slow-replicating compartment). In a second step we also look at the case with competition limited to the environment. One should bear in mind that it also covers the case of competition limited to a host where replication is faster than in the environment (rH  > rE), provided a switch of the H and E index.In the case where competition is limited to only one of the compartments, we do not expect an equilibrium to exist for all traits combination of the phenotypic space. If migration is not sufficiently important, the number of microbes in the unconstrained compartment keeps increasing exponentially faster than the number of microbes in the constrained compartment, which contribution to the whole lineage thus becomes rapidly negligible. At sufficiently high migration rates however, an equilibrium is expected, because microbes switch habitats sufficiently rapidly for competition to be globally effective, although it directly affects only one of the compartments.Competition in the host only (slow-replicating compartment)When there is competition in the host only, there is no (positive) equilibrium for all mH  More

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