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    Predictive model of bulk drag coefficient for a nature-based structure exposed to currents

    The analytical model consists of (1) an adapted drag formulation for closely-packed cylinder arrays, including blockage and sheltering, and (2) a turbulent kinetic energy balance, necessary to quantify sheltering. The turbulence model builds on the formulation suggested by Nepf25 for vegetation canopies, and incorporates a turbulence production term by flow expansion, and an extended drag formulation in the wake production term. The steps to derive the equations are presented below.
    Drag model
    The drag forces experienced by an array of cylinders, per unit mass, can be calculated as:

    $$begin{aligned} F_{d} = frac{1}{2}c_D a |U|U end{aligned}$$
    (1)

    where (c_D) is the drag coefficient of a single cylinder, which can be estimated using the empirical expression of White30, given by:

    $$begin{aligned} c_D = 1 + 10Re^{-2/3} end{aligned}$$
    (2)

    where Re is the Reynolds number based on the cylinder diameter and the depth-averaged local flow velocity U. a is the projected plant area per unit volume, defined by Nepf25 as:

    $$begin{aligned} a = frac{d h}{h s^2} = frac{d}{s^2} end{aligned}$$
    (3)

    with d being the cylinder diameter, s the spacing between cylinders, and h the water depth.
    The main unknown in Eq. (1) is the local flow velocity U. If a cylinder array is sufficiently sparse, the local flow velocity could be assumed equal to the depth-averaged incoming flow velocity, (U_{infty }), either measured or calculated with a free surface flow model. For denser configurations, the velocity will change as the flow propagates through the array due to (1) flow acceleration between the elements (blockage), and (2) flow deceleration due to the sheltering effects of upstream rows of cylinders. Both effects are illustrated in Fig. 1c. The changes in flow velocity are included by multiplying (U_{infty }) by a blockage factor, (f_b), and a sheltering factor, (f_s):

    $$begin{aligned} U = f_b f_s U_{infty } end{aligned}$$
    (4)

    Inserting both factors in the expression for the drag force results in Eq. (5):

    $$begin{aligned} F_{d} = frac{1}{2}c_D a |U|U = frac{1}{2}c_D a f_b^2 f_s^2 |U_{infty }|U_{infty } = frac{1}{2} c_{D,b} a |U_{infty }|U_{infty } end{aligned}$$
    (5)

    where the changes in velocity have been incorporated in the bulk drag coefficient, (c_{D,b} = c_D f_b^2 f_s^2). This expression provides a direct relationship between the drag coefficient of a single cylinder, (c_D), and bulk drag coefficients (c_{D,b}) measured for cylinder arrays in laboratory experiments. Predicting the drag force thus depends on determining the values of (f_b) and (f_s).
    The blockage factor (f_b) can be estimated based on mass conservation through a row of cylinders11, considering that the velocity will increase as the same flow discharge travels through the smaller section between the elements:

    $$begin{aligned} U_{infty } A = U_c A_c = f_b U_{infty } A_c end{aligned}$$
    (6)

    where the total frontal area is (A = h s_y), and (s_y) is the distance between cylinders perpendicular to the flow, center-to-center (see Fig. 1). Subtracting the frontal area of the cylinders from the total area gives the available flow area, (A_c):

    $$begin{aligned} A_c = h s_y – h D = h (s_y-d) end{aligned}$$
    (7)

    Here we are assuming that the water depth is the same just upstream and in between the cylinders. Solving for (f_b) in Eq. (6) results in Eq. (8), see also Etminan et al.11:

    $$begin{aligned} f_b = frac{h s_y}{ h (s_y-d)} = frac{1}{1-d/s_y} end{aligned}$$
    (8)

    The sheltering factor (f_s) can be estimated from the wake flow model by Eames et al.26, which predicts the velocity deficit behind a cylinder as a function of the distance downstream of the cylinder, (s_x), the cylinder diameter, the local turbulent intensity (I_t), and the drag coefficient:

    $$begin{aligned} frac{U_{infty }-U_{w}}{U_{infty }} = frac{c_D d}{2sqrt{2 pi } I_t s_x} end{aligned}$$
    (9)

    where (U_{w}) is the velocity in the cylinder wake, (U_{infty }) is the incoming flow velocity, and (I_t) is the meant turbulent intensity, defined as (I_t = sqrt{k}/U_{infty })21,25. k represents the turbulent kinetic energy per unit mass, with (k = 1/2(overline{u’^{2}} + overline{v’^{2}} + overline{w’^{2}})), where (u’), (v’), and (w’) are the instantaneous velocity fluctuations in the streamwise, lateral, and vertical direction respectively, and where the overbar denotes time averaging. The turbulent velocity fluctuations are defined as the difference between the instantaneous velocities and their mean value over a measurement period. Here we consider the depth-averaged value of the turbulent intensity, in view of the uniformity of the turbulent properties over the vertical observed inside emergent arrays25.
    Equation (9) was developed assuming turbulent flow. Viscous effects decrease the velocity deficit26, with the reduction factor being given by:

    $$begin{aligned} f_{Re} = sqrt{frac{Re}{Re_{t}}} end{aligned}$$
    (10)

    where (Re_{t}) is the lowest Reynolds number corresponding to fully turbulent wake flow. Laminar effects are included in the wake flow model by multiplying the velocity deficit of Eq. (9) by the reduction factor (f_{Re}) for (Re < Re_t), where the the turbulent Reynolds number is assumed equal to (Re_t = 1,000). This value is based on the observation that although a wake starts becoming turbulent at (Re_{t} sim 200), drag coefficient measurements usually become constant at Reynolds numbers beyond (Re_{t} sim 1000), e.g. as shown in Figure 2.7 of Sumer and Fredsoe13. The influence of varying (Re_{t}) on the model results is investigated in “Results and discussion” section. Defining the sheltering factor as (f_s = frac{U_{w}}{U_{infty }}), and including (f_{Re}) and the bulk drag coefficient in the definition of the velocity deficit results in Eq. (11): $$begin{aligned} f_s = frac{U_{w}}{U_{infty }} = 1-f_{Re}frac{c_{D,b} d}{2sqrt{2 pi } I_t s_x} = 1-f_{Re}frac{c_{D,b} d}{2sqrt{2 pi } (sqrt{k}/U_{infty }) s_x} end{aligned}$$ (11) Equation (9) also assumes that the downstream cylinder is placed inside the ballistic spreading region of the wake. The ballistic regime occurs for a downstream distance (s_x < L/It), where L is the integral length-scale of turbulence, and it is characterized by a rapidly decaying velocity deficit, and by a linear increase of the wake width with downstream distance. Inside the cylinder arrays, the length scale development is limited by the downstream spacing, resulting in (L < s_x). Considering that turbulent intensity measurements of Jansen29 varied between (I_t) = 0 and 0.8 inside cylinder arrays with n = 0.64–0.9, this would result in (L < s_x/It). This is a reasonable general assumption for the bamboo structures, since their porosity varies in a similar range. If the poles were sparsely placed, there would be a transition from ballistic to diffusive spreading of the wake. Eames et al.26 also developed expressions for turbulent flow under the diffusive regime, which could be used in place of Eq. (9). In the opposite case of very high pole densities, there may be a point where the elements are so closely-packed that vortex shedding is inhibited by the presence of the neighboring cylinders. Considering an analogy with a cylinder placed close to a solid boundary, vortex shedding would not take place for spanwise spacings smaller than (s_y/d < 1.3)13, causing a decrease of the drag coefficient that would not be reproduced by the expression of White30. The application of the present model is thus restricted to (s_y/d > 1.3).
    Balance of turbulent kinetic energy
    Application of Eq. (11) requires predicting the turbulent kinetic energy. This is calculated by expanding the model developed by Nepf25, based on a balance between turbulence production and dissipation:

    $$begin{aligned} P_w sim epsilon end{aligned}$$
    (12)

    where (P_w) is the turbulent production rate and (epsilon) is the dissipation rate. For a dense cylinder array, k is produced by (1) generation in the wakes of the cylinders25, and (2) shear production by the jets formed between the elements28. The total turbulence production term, (P_w), consequently has two parts:

    $$begin{aligned} P_w = P_{w1}+P_{w2} end{aligned}$$
    (13)

    We assume that for dense cylinder arrays these two terms are much higher than turbulence production by shear at the bed, based on observations by Nepf25 for sparse arrays. This assumption is further tested in “Results and discussion” section.
    The first term in Eq. (13), (P_{w1}), represents turbulence production at the wakes, and can be estimated as the work done by the drag force times the local flow velocity:

    $$begin{aligned} P_{w1} = frac{1}{2}c_D a |U|U^2 = frac{1}{2}c_D a f_b^3 f_s^3 |U_{infty }|U_{infty }^2 end{aligned}$$
    (14)

    The second term, (P_{w2}), represents turbulence generation due to flow expansion28, and can be estimated from the Reynolds shear stresses:

    $$begin{aligned} P_{w2} = overline{ u’ v’} frac{partial u }{partial y} end{aligned}$$
    (15)

    where the overbar denotes time averaging. The loss in mean kinetic energy (E_c) due to flow expansion is equal to:

    $$begin{aligned} Delta E_c = frac{1}{2} U_{infty }^2 left( left( frac{A}{A_c}right) ^{2}-1 right) = frac{1}{2} left( f_b^{2}-1 right) U_{infty }^2 end{aligned}$$
    (16)

    where the energy loss due to flow expansion, (Delta E_c), is modelled using the Carnot losses. Assuming that the mean kinetic energy is transformed into turbulent kinetic energy (E_t), and assuming isotropic turbulence, gives Eq. (17):

    $$begin{aligned} frac{1}{2} left( f_b^{2}-1 right) U_{infty }^2 = frac{3}{2}overline{ u’ u’} end{aligned}$$
    (17)

    Equation (17) enables expressing the normal Reynolds stress as a function of the incoming flow velocities and the blockage factor:

    $$begin{aligned} overline{ u’ u’} = frac{1}{3} left( f_b^{2}-1 right) U_{infty }^2 end{aligned}$$
    (18)

    The Reynolds shear stress is estimated as (overline{ u’ v’} = Roverline{ u’ u’}), where the correlation factor R was given a constant value of 0.4 based on observations of Nezu and Nakagawa31. This value was derived for open channel flow conditions and is assumed acceptable as a first approximation, but it could vary inside a cylinder array. This is explored further in “Results and discussion” section.
    The velocity gradient is estimated from the velocity difference between the side of the cylinders (dominated by blockage) and the wake of the cylinders (dominated by sheltering) resulting in Eq. (19):

    $$begin{aligned} frac{partial u }{partial y} approx frac{U_{infty }(f_b-f_s)}{frac{1}{2} s_y} end{aligned}$$
    (19)

    Substitution into Eq. (15) gives Eq. (20):

    $$begin{aligned} P_{w2} = frac{2}{3} R (f_b-f_s)(f_b^{2}-1)frac{U_{infty }^3}{s_y} end{aligned}$$
    (20)

    The dissipation term, (epsilon), is estimated as:

    $$begin{aligned} epsilon sim k^{3/2} l^{-1} end{aligned}$$
    (21)

    The characteristic turbulent length scale l is limited by the surface-to-surface separation between the elements in the flow direction, (l = min(|s_x-d|, d)). This differs from the expression developed by Nepf25, who used the diameter as representative of the size of the eddies. We assume that in closely-packed cylinder arrays the spacing between cylinders may be smaller than the diameter, (|s_x-d| < d), which would limit turbulence development. The maximum value of l is set equal to the cylinder diameter. Here we also assume that for the dense cylinder arrangements, the spacing between cylinders is considerably smaller than the water depth, hence turbulence generated by bed friction is negligible. Balancing the production and dissipation of turbulent kinetic energy results in Eq. (22): $$begin{aligned} frac{k^{3/2}}{l} sim |U_{infty }|U_{infty }^2left( c_D a f_b^3 f_s^3 + frac{ 4R}{3s_y}(f_b^{2}-1)(f_b-f_s)right) end{aligned}$$ (22) Taking the cubic root at both sides and introducing the scale factor (alpha _1) gives Eq. (23): $$begin{aligned} frac{sqrt{k}}{U_{infty }} = alpha _1left( c_D f_b^3 f_s^3 a l + frac{4}{3}R(f_b^{2}-1)(f_b-f_s)frac{ l}{s_y}right) ^{1/3} end{aligned}$$ (23) Where (alpha _1) is a coefficient of ({mathcal {O}}(1)), which is given a default value of (alpha _1 = 1). The sensitivity of the model to different (alpha _1) and R values is explored in “Results and discussion” section. k can be calculated by solving Eq. (23) iteratively, using the incoming upstream velocity (U_{infty }) and the geometric characteristics of the structure, (s_y, s_x, d) and a, as an input. This enables determining the sheltering factor, (f_s = U_{w}/U_{infty }) from Eq. (11). The blockage factor (f_b=(1-d/s_y)^{-1}) can also be calculated from the geometric properties of each configuration. Both coefficients can be then combined to predict the bulk drag coefficient, with (c_D,_{b} =c_D(f_s)^2(f_b)^2). Deriving (c_D,_{b}) with the present approach relies on the assumption that the changes in water depth through the structure are small. This is a reasonable assumption given the short length of the bamboo structures in the streamwise direction, which varies between 0.7 and 1.5 m (see Fig. 1b). Longer structures that experience non-negligible changes in flow depth and velocity should be discretized, and the bulk drag coefficient should be calculated separately for the different sections. The model assumptions are discussed further in the following section. More

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    Increasing flavonoid concentrations in root exudates enhance associations between arbuscular mycorrhizal fungi and an invasive plant

    Seeds collection and germination
    We collected T. sebifera seeds by hand from populations in both the introduced (US—16 populations in total) and native (China—14 populations in total) ranges (for details see Table S1). At each population, we haphazardly selected 5–10 trees, and harvested thousands of seeds from each tree. In the laboratory, we removed the waxy coats around these seeds by hand after immersing them in a mixture of water and laundry detergent (10 g/L) for 24 h [29]. Then, we rinsed, surface sterilized (10% bleach), and dried them. In order to improve germination, we put these seeds in wet sand and stored them in the refrigerator (4 °C) for at least 30 days. In spring, we sowed these seeds in greenhouse trays (50 holes/tray) which were filled with sterilized (autoclave at 121 °C for 30 min) commercial potting soil, and then kept them in an open-sided greenhouse at Henan University in Kaifeng, Henan, China (34°49′13′′ N, 114°18′18′′ E) or unheated greenhouse at Rice University, Houston, TX USA (29°43′08′′ N 95°24′11′′ W). After seeds germinated and seedlings reached the 4 true leaf stage, we selected similar size seedlings to conduct the following experiments.
    Common garden experiment—differences in AM fungal colonization and plant growth
    To investigate the differences in AM fungal colonization and growth between plants from introduced (US) and native populations (CH), we carried out a common garden experiment at Henan University. We collected soil in a corn field, which includes most common AM fungal species based on previous reports [33, 34]. It was a sandy soil with total nitrogen and total phosphorus of 1.9 g/kg (DW) and 0.6 g/kg (DW), respectively, and pH of 7.68. We removed surface litter before collecting topsoil (10–15 cm depth) and then combined equal parts of soil and fine sand in 132 pots (21 cm × 16 cm, ~3 kg of soil mix each) after they were passed through a 1-cm mesh screen. We planted seedlings from 22 populations (12 native and 10 introduced populations, 6 seedlings of each population, Table S1) individually in these prepared pots and placed them in the open-sided greenhouse. We protected them from herbivores with nylon mesh (16 openings/cm) cages during the experiment. After 60 and 90 days, we harvested 3 seedlings from each population as 3 reps each time and carefully washed their whole roots from the soil. We collected ~30 fresh fine roots ( >1 cm/segment) from each plant root to measure AM fungal colonization. In brief, we cleared (in 10% KOH), bleached, acidified, and stained (trypan blue) these samples then slide mounted 30 one cm long pieces of fine root for each plant [7]. AM fungal colonization was determined by the gridline intersect method with 300 intersection points per plant [35]. We dried and weighed the roots and shoots.
    Collection of root exudates and flavonoids analysis for root exudates
    Our previous study found higher concentrations of flavonoids but lower concentrations of tannins in roots of introduced populations of T. sebifera than in native populations [17] with quercetin and quercitrin being the main flavonoids [28, 30]. In our pilot experiment, we only detected quercetin and quercitrin in root exudates but no other flavonoids. Therefore, in this study we focused on quercetin and quercitrin in root exudates and their functions. We determined their amounts in root exudates from native (China) and introduced (US) populations at Henan University. We filled 132 glass beakers (1000 ml) with Hoagland’s solution [36] and covered the opening with a foam board with a hole in its center. We took 6 seedlings from each of 22 populations (12 native, 10 introduced, Table S1) and carefully washed the soil from their roots with tap water, then transplanted them individually into the beakers (1 seedling per beaker) and fixed them with a sponge. Because of mortality, only 80 plants of 17 populations (9 native, 8 introduced) survived until exudate collection. The odds of a plant dying did not depend on population origin (F1,20 = 3.7, P = 0.0679) or population (Z = 1.3, P = 0.0937). We checked these glass beakers and filled them with Hoagland’s solution every day.
    After these plants grew for 57 or 87 days in an open-sided greenhouse with a typical temperature range of 18 °C (night) to 28 °C (day) and 13–14 h of natural daylight, we put DI water into these beakers instead of Hoagland’s solution to minimize the effects of environments on root exudates. Three days later (i.e., at 60 and 90 days) these plants were harvested to obtain their dry root mass. The root exudates were dried at 40 °C under vacuum by rotary evaporators. Then we extracted the chemicals from these concentrates in 3 ml of methanol solution with 0.4% phosphoric acid water (48:52, v:v) and filtered them through 0.22 μm hydrophobic membranes. The concentrations of quercetin and quercitrin were assessed by high-performance liquid chromatography [30]. In brief, 20 μl of extract was injected into an HPLC with a ZORBAX Eclipse C18 column (4.6 × 250 mm, 5 μm; Agilent, Santa Clara, CA, USA) with the following flow: 1.0 mL min−1 with a 100% methanol (B) and 0.4% phosphoric acid in water (A) as the mobile phase. The gradient was as follows: 0–10 min 52:48 (A:B); 10–24 min 48:52 (A:B). UV absorbance spectra were recorded at 254 nm. The concentrations of flavonoid compounds were calculated and standardized by peak areas of standards of known concentrations.
    Root exudate addition experiment—effects of different populations on AM fungal colonization
    In order to investigate the role that root exudates play in the interactions between AM fungi and plants, we conducted an experiment in which exudates were collected from plants in liquid (donor) and applied to the soils of other plants (target). The exudate donor plants were grown in 1080 (two venues: 540 seedlings at Rice University and 540 seedlings at Henan University) containers, each with 1000 ml of Hoagland’s solution, that each had a foam board top with a hole and a bottom drain tube that could be regulated. At each venue, we washed the soil from ~500 sets of plants (US = 465, China = 504) from native (8 populations for venue US and 7 populations for venue CH) or introduced (13 populations for venue US and 12 populations for venue CH) populations and secured them (3 plants per container) in the containers using sponges (details in Table S1). The remaining containers were left as plant-free controls. We started the application experiment after 7 days.
    For exudate target plants, we collected the soil from different sites in the introduced or native ranges (See Table S1). At each site, we collected soil under the canopy of a T. sebifera tree (Home soil) and that more than 3 meters away from the canopy of a T. sebifera tree (Away soil). We collected the topsoil to a depth of 15 cm after removing the surface litter, air dried them, and screened them (1 cm mesh). These soils were mixed with vermiculite (1:2 volume). Then we used these mixes to fill 1080 pots (15 cm × 12 cm; 540/venue). Each pot in China received a mixed soil from a site in China and each pot in US received soil from a single small area within a site in the US. We transplanted a seedling from a native (12 populations for venue US and 3 populations for venue CH, See Table S1) or introduced (13 populations for venue US and 5 populations for venue CH, See the Table S1) population into each pot (270 of each per venue). We randomly assigned a target plant to each set of donor plants or water only controls.
    Every 4 days we changed the Hoagland’s solution to DI water for 3 days to collect root exudates from donor plants. Then we applied this water solution from a donor set to its target plant. After 70 days, we harvested the target seedlings, kept a fine root sample for AM fungal colonization determination, then dried and weighed leaves, stems, and roots.
    Chemical addition experiment—quercetin and quercitrin effects on AM fungal colonization
    We transplanted 391 seedlings from 8 native populations (CH) and 9 introduced populations (US) into 391 pots with field soil (1.3 kg/pot) in nylon mesh cages at Henan University. To test the effect of quercetin and quercitrin on AM fungal colonization, we prepared solution of quercetin or quercitrin in acetone (10 mg/mL) (acetone did not affect AM fungal colonization based on our preliminary experiment). Then these solutions were diluted in water to 2 concentrations (1 mg/L and 10 mg/L) based on the result of chemical analyses of root exudates and the 0.1% of acetone in water as control. We watered 15 ml of solution (5 reps per population) or water (3 reps per population) around the base of seedling stems every 3 days (16 times in total). Four plants died (3 in quercitrin application treatment, 1 in quercetin application treatment). After 70 days, we collected seedlings by cutting at ground level and collected fine roots to test AM fungal colonization.
    Activated carbon experiment—AM fungal colonization with inactivated chemicals
    In order to verify the chemicals in root exudates play a key role in the relationship between AM fungi and plant roots, we conducted an experiment at Henan University with activated carbon (AC) addition to block bioactivity of root exudate chemicals. We filled plastic pots in mesh cages at Henan University with either 1.3 kg of field soil (N = 78) or field soils amended with activated carbon (N = 78, Sinopharm Chemical Reagent Co., Ltd, Beijing, China) added as 1:500 v:v. We transplanted seedlings from 13 populations (6 native and 7 introduced, Table S1) into the pots with 6 seedlings for each population. Eighteen seedlings died during this experiment (12 seedlings from AC treatment, 6 seedlings from control). After 70 days, we harvested plants and used a fine root sample to determine AM fungal colonization.
    Field survey of AM fungal assemblages
    We collected rhizosphere soil from 3 sites in China (Dawu, Hubei, 31°28′N, 114°16′E; Wuhan, Hubei, 30°32′N, 114°25′E; Guilin, Guangxi, 25°04′N, 110°18′E) for AM fungal species identification via high-throughput sequencing. At each of these sites, we selected 3 T. sebifera trees per site and dug the soil close to the tree trunk until its root branch was found. We collected soils from 3 roots per plant. We removed the bulk soil from these roots by shaking, and then collected the soil remaining on these roots using brushes (1 new brush per tree). The rhizosphere soils on the roots from same tree were mixed fully. About 5 g of fresh rhizosphere soil from one tree was collected and stored in dry ice and ultra-low temperature freezer (−80 °C) until they were used to test the AM fungi abundance based on high-throughput sequencing [37, 38].
    For DNA extraction, microbial DNA was extracted from the prepared samples (0.25 g soil per sample) using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer’s protocols. The DNA concentration and purification were determined by NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was checked by 1% agarose gel electrophoresis [39].
    For the PCR amplification, nested PCR was conducted to amplify the AM fungi 18S rRNA. The primer pairs AML1 (5′-ATCAACTTTCGATGGTAGGATAGA-3′) and AML1 (5′-GAACCCAAACACTTTGGTTTCC-3′) were used in the first run. The primer pairs AMV4.5NF (5′-AAGCTCGTAGTTGAATTTCG-3′) and AMDGR (5′-CCCAACTATCCCTATTAATCAT-3′) were used in the second run in the thermocycler PCR system (GeneAmp 9700, ABI, USA). The PCR reactions were conducted using the program according to Xiao et al. [39].
    For each sample, purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols of Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw fastq files were quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) the reads were truncated at any site receiving an average quality score More

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    Seventy years of data from the world’s longest grazed and irrigated pasture trials

    Experimental design
    The Winchmore Irrigation Research Station is in the centre of the Canterbury plains, the largest area of flat land in New Zealand (43.787° S, 171.795° E; Fig. 1). It is at an altitude of 160 m above sea level, a mean annual temperature of 12 °C, and has an annual rainfall of 745 mm (range 491–949 mm)20. The soil is a Lismore stony silt loam classified as an Orthic Brown soil in the New Zealand soil classification and as an Udic Ustochrept in USDA soil classification21. Flood irrigation, known locally as border-check/dyke irrigation, was installed at the site in 1947. However, the two long-term trials, hereafter known as the fertiliser and irrigation trials, were established in 1952 and 1949, respectively.
    Fig. 1

    Location of Winchmore within the Canterbury region (coloured green) and the layout of the long-term fertiliser and irrigation trials over time.

    Full size image

    Full details of the setup of the fertiliser and irrigation trials between 1949–1951, including the political rationale for the trial, its statistical design, cultivation dates, sowing rates of perennial ryegrass (Lolium spp) and white clover (Trifolium repens) and initial fertiliser and irrigation treatments are available elsewhere20.
    The fertiliser trial has 20 border check irrigation bays divided into five treatments each with four replicates set out in a randomised block design (Fig. 1). From 1952/53 to 1957/1958 treatments were: nil (no P applied), 188, 376 (annually and split P applications), and 564 kg SPP ha−1. All P applications occurred annually in autumn except for the 376 kg SSP ha−1 treatment which had two treatments divided into an annual autumn application and split applications in between autumn and spring. From 1958/59 to 1979–80 the nil and 188 and 376 (split autumn and spring application) SSP treatments were unaltered, while P applications were stopped to the annual 376 and 564 SSP treatments. In 1972, 4.4 t/ha of lime (caclium carbonate) was applied to all treatments22. From 1980 onwards the nil, and 188 SSP treatments and the 376 SSP treatment, now receiving winter fertiliser applications, were joined by a treatment applying 250 SSP ha−1 in winter to the previous 376 SSP treatment and a Sechura rock phosphate treatment applying 22 kg P ha−1 in winter to the former 576 SSP treatment.
    Each irrigation bay was fenced off, 0.09 ha in size and grazed by separate mobs of sheep at 6, 11, and 17 stock units (with one stock unit equivalent to one ewe at 54 kg live-weight) per replicate for the nil, 188 SSP, and 376 SSP treatments, respectively. This separation prevented carry-over of dung P and other nutrients and contaminants between treatments. No grazing occurred in winter. Flood irrigation was applied when soil moisture content (w w−1) fell below 15% (0–100 mm depth). This occurred on-average 4.3 times per year.
    The irrigation trial had 24 irrigation bays (each 0.09 ha in size) which had lime applied to the whole trial in 1948 (5 t ha−1) and 1965 (1.9 t ha−1) to maintain soil pH at 5.5–6.0. From 1951 to 1954 treatments were SSP applied at 250 kg ha−1 in autumn annually and either four replicates of dryland, or five replicates of irrigation applied at one, two, three, six-weekly intervals or at three-weekly intervals in alternate seasons. From 1953/54 to 1956/57 the weekly and two-weekly treatments were replaced by irrigation when soil moisture in the top 100-mm of soil reach 50 and 0% available soil moisture (asm), respectively. In 1958 the irrigation trial was cultivated with a rotary hoe and grubber, 140 kg SSP ha−1 applied and the site re-sown in ryegrass and white clover. From 1958/59–2007 the site had the same blanket application of SSP and four replicates of dryland, while a completely randomised design was used to impose five replicates of four treatments (Fig. 1) that looked at irrigation applied when soil moisture in the top 100-mm of soil reach 10, 15 and 20% (equivalent to 50% asm with 0% asm being wilting point) and irrigation on a 21-day interval. The need for irrigation to the irrigation and fertiliser trials was informed by soil moisture measured weekly by technical staff using a mixture of gravimetric analyses (1950–1985), neutron probe (1985–1990) and time-domain reflectometer (1990-onwards). Irrigation was applied at a rate of 100 mm per event20.
    Except for winter, when no grazing occurred, each treatment was rotationally grazed by a separate flock of sheep with 6 and 18 stock units per replicate for the dryland and 20% v/v treatments, respectively.
    The irrigation trial finished in October 2007 although the P fertiliser regime continued. All irrigated treatments shifted to the same three weekly schedule as the long-term Fertiliser trial. The dryland treatment remained unirrigated. The Winchmore farm was converted into a commercial irrigated farm operation and sold in 2018. The fertiliser trial was also sold but with a covenant ensuring it continues to operate as per normal except that irrigation from 2018 onwards is now applied by spray irrigation with the aim of ensuring soil moisture is maintained above 90% of field capacity. Since January 2019 there are daily soil moisture meter records from a moisture meter installed into one of the control plots. Soil moisture, rainfall and irrigation are recorded.
    The production of the Winchmore trials data records23 involved a three-step process (Fig. 2).
    Fig. 2

    Flowchart of the steps involved in sampling, analysis, collation and curation and data analysis and processing of the databases from the Winchmore Trials. Note that blue and orange boxes are sub tasks associated with each step and resulting outputs, respectively.

    Full size image

    Step 1: Soil and pasture sampling
    Pasture production was measured from two exclusion cages (3.25 m long × 0.6 m wide) per plot24. Areas within each cage were trimmed to 25 mm above ground level and left for a standard grazing interval for that time of year. Following grazing a lawnmower was used to harvest a 0.40 m wide strip in the middle of each enclosure to 25 mm above ground level. The wet weight was determined, and a sub-sample taken to determine dry matter content with a separate sample manually dissected into grass, clover and weeds. All surplus mown herbage was returned to the plot. Approximately 9–10 cuts were made annually. A composite soil sample of 10 cores (2.5 cm diameter and 7.5 cm deep) was collected from each plot. These were collected four times annually (July, prior to fertiliser application, and October, January and April), using established best practices24,25. In 2009 soil samples were also collected from the 0–75, 75–150, 150–250, 250–500, 500–750, and 750–1000 mm depths on both trials17. During 2018, prior to cultivation, soil on the unirrigated, 10 and 20% soil moisture treatments of the irrigation trial were sampled at 0–150, 150–250, 250–500, 500–750, 750–1000, 1000–1500, and 1500–2000 mm depths. The top 250 mm of these samplings were collected by hand using an auger, but deeper depths were excavated via a mechanical digger. Representative sub-samples were taken from each depth. Annual samplings were crushed, dried and sieved More

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    Variable inter and intraspecies alkaline phosphatase activity within single cells of revived dinoflagellates

    1.
    Gobler CJ, Doherty OM, Hattenrath-Lehmann TK, Griffith AW, Kang R, Litaker W. Ocean warming has expanded niche of toxic algae. Proc Natl Acad Sci USA. 2017;114:4975–80.
    CAS  PubMed  Article  Google Scholar 
    2.
    Olivieri ET. Colonization, adaptations and temporal changes in diversity and biomass of a phytoplankton community in upwelled water off the Cape Peninsula, South Africa, in December 1979. South Afr J Mar Sci. 1983;1:77–109.
    Article  Google Scholar 

    3.
    Irwin AJ, Zoe V, Finkel ZV, Müller-Karger FE, Troccoli, Ghinaglia L. Phytoplankton adapt to changing ocean environment. Proc Natl Acad Sci USA. 2015;112:5762–6.
    CAS  PubMed  Article  Google Scholar 

    4.
    Chivers W, Walne A, Hays G. Mismatch between marine plankton range movements and the velocity of climate change. Nat Commun. 2017;8:14434.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    5.
    Gisselson L, Granéli E, Pallon J. Variation in cellular nutrient status within a population of Dinophysis norvegica (Dinophyceae) growing in situ: single – cell elemental analysis by use of a nuclear microprobe. Limnol Oceanogr. 2001;5. https://doi.org/10.4319/lo.2001.46.5.1237.

    6.
    Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508. https://doi.org/10.1038/nrmicro3491.
    CAS  Article  PubMed  Google Scholar 

    7.
    Núñez-Milland DR, Baines SB, Vogt S, Twining BS. Quantification of phosphorus in single cells using synchrotron X-ray fluorescence. J Synchrotron Radiat. 2010;17:560–6.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    8.
    Berthelot H, Duhamel S, L’Helguen S, Maguer JF, Wang S, Cetinić I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651–62. https://doi.org/10.1038/s41396-018-0285-8.
    CAS  Article  PubMed  Google Scholar 

    9.
    Štrojsová A, Vrba J. Short-term variation in extracellular phosphatase activity: possible limitations for diagnosis of nutrient status in particular algal populations. Aquat Ecol. 2009;43:19–25.
    Article  CAS  Google Scholar 

    10.
    O’Donnell DR, Hamman CR, Johnson EC, Kremer CT, Klausmeier CA, Litchman E. Rapid thermal adaptation in a marine diatom reveals constraints and trade-offs. Glob Change Biol. 2018;24:4554–65.
    Article  Google Scholar 

    11.
    Jin P, Agustí S. Fast adaptation of tropical diatoms to increased warming with trade-offs. Sci Rep. 2018;8:17771. https://doi.org/10.1038/s41598-018-36091-y.
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    12.
    Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, et al. Extinction risk from climate change. Nature. 2004;427:145–8.
    CAS  PubMed  Article  Google Scholar 

    13.
    Urban MC. Accelerating extinction risk from climate change. Science. 2015;348:571–3.
    CAS  PubMed  Article  Google Scholar 

    14.
    Kottuparambil S, Jin P, Agusti S. Adaptation of Red Sea Phytoplankton to experimental warming increases their tolerance to toxic metal exposure. Front Environ Sci. 2019;7. https://doi.org/10.3389/fenvs.2019.00125.

    15.
    Flores-Moya A, Costas E, Lopez-Rodas V. Roles of adaptation, chance and history in the evolution of the dinoflagellate Prorocentrum triestinum. Naturwissenschaften. 2008;95:697–703.
    CAS  PubMed  Article  Google Scholar 

    16.
    Flores-Moya A, Rouco M, García-Sánchez MJ, García-Balboa C, González R, Costas E, et al. Effects of adaptation, chance, and history on the evolution of the toxic dinoflagellate Alexandrium minutum under selection of increased temperature and acidification. Ecol Evol. 2012;2:1251–9. https://doi.org/10.1002/ece3.198.
    Article  PubMed  PubMed Central  Google Scholar 

    17.
    Martiny AC, Ustick LA, Garcia C, Lomas MW. Genomic adaptation of marine phytoplankton populations regulates phosphate uptake. Limnol Oceanogr. 2019. https://doi.org/10.1002/lno.11252.

    18.
    Ribeiro S, Berge T, Lundholm N, Andersen TJ, Abrantes F, Ellegaard M. Phytoplankton growth after a century of dormancy illuminates past resilience to catastrophic darkness. Nat Commun. 2011;2:311.
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Delebecq G, Schmidt S, Ehrhold A, Latimier M, Siano R. Revival of ancient marine dinoflagellates using molecular biostimulation. J Phycol. 2020;56:1077–89.
    CAS  PubMed  Article  Google Scholar 

    20.
    Ribeiro S, Berge T, Lundholm N, Ellegaard M. Hundred years of environmental change and phytoplankton ecophysiological variability archived in coastal sediments. PLoS ONE. 2013;8:e61184.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Klouch KZ, Schmidt S, Andrieux Loyer F, Le Gac M, Hervio-Heath D, Qui-Minet ZN, et al. Historical records from dated sediment cores reveal the multidecadal dynamic of the toxic dinoflagellate Alexandrium minutum in the Bay of Brest (France). FEMS Microbiol Ecol. 2016;92:1–16.
    Article  CAS  Google Scholar 

    22.
    Lundholm N, Ribeiro S, Godhe A, Rostgaard Nielsen L, Ellegaard M. Exploring the impact of multidecadal environmental changes on the population genetic structure of a marine primary producer. Ecol Evol. 2017;7:3132–42.
    PubMed  PubMed Central  Article  Google Scholar 

    23.
    Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.
    CAS  Article  Google Scholar 

    24.
    Labry C, Herbland A, Delmas D. The role of phosphorus on planktonic production of the Gironde plume waters in the Bay of Biscay. J Plankt Res. 2002;24:97–117.
    CAS  Article  Google Scholar 

    25.
    Girault M, Arakawa H, Hashihama F. Phosphorus stress of microphytoplankton community in the western subtropical North Pacific. J Plankt Res. 2013;35:146–57.
    CAS  Article  Google Scholar 

    26.
    Ramos JBE, Schulz KG, Voss M, Narciso Á, Müller MN, Reis FV, et al. Nutrient-specific responses of a phytoplankton community: a case study of the North Atlantic Gyre. Azores J Plankt Res. 2017;39:744–61.
    Article  CAS  Google Scholar 

    27.
    Lin S, Litaker RW, Sunda WG. Phosphorus physiological ecology and molecular mechanisms in marine phytoplankton. J Phycol. 2016;52:10–36.
    CAS  PubMed  Article  Google Scholar 

    28.
    Lomas MW, Swain A, Shelton R, Ammerman JW. Taxonomic variability of phosphorus stress in Sargasso Sea phytoplankton. Limnol Oceanogr. 2004;49:2303–10.
    Article  Google Scholar 

    29.
    Wang D, Huang B, Liu X, Liu G, Wang H. Seasonal variations of phytoplankton phosphorus stress in the Yellow Sea Cold Water Mass. Acta Oceano Sin. 2014;33:124–35.
    Article  CAS  Google Scholar 

    30.
    Cembella AD, Antia NJ, Harrison PJ. The utilization of inorganic and organic phosphorous compounds as nutrients by eukaryotic microalgae: a multidisciplinary perspective: part I. CRC Crit Rev Microbiol. 1984;10:317–91.
    CAS  Article  Google Scholar 

    31.
    Cooper A, Bowen ID, Lloyd D. The properties and subcellular localization of acid phosphatases in the colourless alga Polytomella caeca. J Cell Sci. 1974;15:605–18.
    CAS  PubMed  Google Scholar 

    32.
    Duhamel S, Björkman KM, Van Wambeke F, Moutin T, Karl DM. Characterization of alkaline phosphatase activity in the North and South Pacific Subtropical Gyres: Implications for phosphorus cycling. Limnol Oceanogr. 2011;56:1244–54.
    CAS  Article  Google Scholar 

    33.
    Kang W, Wang ZH, Liu L, Guo X. Alkaline phosphatase activity in the phosphorus-limited southern Chinese coastal waters. J Environ Sci. 2019;86:38–49.
    Article  Google Scholar 

    34.
    Girault M, Beneyton T, Pekin D, Buisson L, Bichon S, Charbonnier C, et al. High-content screening of plankton alkaline phosphatase activity in microfluidics. Anal Chem. 2018;90:4174–81. https://doi.org/10.1021/acs.analchem.8b00234.
    CAS  Article  PubMed  Google Scholar 

    35.
    Anderson RA, Berges RA, Harrison PJ, Watanabe MM. Appendix A – recipes for freshwater and seawater media; enriched natural seawater media. In Andersen RA, editor. Algal culturing techniques. San Diego, USA: Academic; 2005. p. 429–538.

    36.
    Guillard RL, Ryther JH. Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt, and Detonula confervacea (cleve) Gran. Can J Microbiol. 1962;8:229–39.
    CAS  PubMed  Article  Google Scholar 

    37.
    Duffy DC, McDonald JC, Schueller OJ, Whitesides GM. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal Chem. 1998;70:4974–84.
    CAS  PubMed  Article  Google Scholar 

    38.
    Girault M, Hattori A, Kim H, Arakawa H, Matsuura K, Odaka M, et al. An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution. Sci Rep. 2017;7:40072. https://doi.org/10.1038/srep40072.
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    39.
    Girault M, Odaka M, Kim H, Matsuura K, Terazono H, Yasuda K. Particle recognition in microfluidic applications using a template matching algorithm. JPN J Appl Phys. 2016;55. https://doi.org/10.7567/JJAP.55.06GN05.

    40.
    Urvoy M, Labry C, Delmas D, Creac’h L, L’Helguen S. Microbial enzymatic assays in environmental water samples: impact of Inner Filter Effect and substrate concentrations. Limnol Oceanogr Methods. 2020;18:728–38.
    Article  CAS  Google Scholar 

    41.
    Huang Z, Terpetschnig E, You W, Haugland RP. 2-(2′-phosphoryloxyphenyl)-4-(3H)-quinazolinone derivatives as fluorogenic precipitating substrates of phosphatases. Anal Biochem. 1992;207:32–39.
    CAS  PubMed  Article  Google Scholar 

    42.
    Girault M, Hattori A, Kim H, Matsuura K, Odaka M, Terazono H et al. Algorithm for the precise detection of single and cluster cells in microfluidic applications. Cytom Part A. 2016. https://doi.org/10.1002/cyto.a.22825.

    43.
    Murphy J, Riley JP. A modified single solution method for the determination of phosphate in natural waters. Anal Chim Acta. 1962;27:31–36.
    CAS  Article  Google Scholar 

    44.
    Hoppe HG. Phosphatase activity in the sea. Hydrobiologia. 2003;493:187–200.
    CAS  Article  Google Scholar 

    45.
    Golda-VanEeckhoutte RL, Roof LT, Needoba JA, Peterson DT. Determination of intracellular pH in phytoplankton using the fluorescent probe, SNARF, with detection by fluorescence spectroscopy. J Microbiol Methods. 2018;152:109–18.
    CAS  PubMed  Article  Google Scholar 

    46.
    Kruskopf MM, Du Plessis S. Induction of both acid and alkaline phosphatase activity in two green-algae (chlorophyceae) in low N and P concentrations. Hydrobiologia. 2004;513:59–70.
    CAS  Article  Google Scholar 

    47.
    Štrojsová A, Vrba J, Nedoma J, Komárková J, Znachor P. Seasonal study of extracellular phosphatase expression in the phytoplankton of a eutrophic reservoir. Eur J Phycol. 2003;38:295–306.
    Article  CAS  Google Scholar 

    48.
    Skelton HM, Parrow MW, Burkholder JM. Phosphatase activity in the heterotrophic dinoflagellate Pfiesteria shumwayae. Harmful Algae 2006;5:395–406.
    CAS  Article  Google Scholar 

    49.
    Nedoma J, Štrojsová A, Vrba J, Komárková J, Simek K. Extracellular phosphatase activity of natural plankton studied with ELF97 phosphate: fluorescence quantification and labelling kinetics. Environ Microbiol. 2003;5:462–72.
    CAS  PubMed  Article  Google Scholar 

    50.
    Young EB, Tucker RC, Pansch LA. Alkaline phosphatase in freshwater Cladophora-epiphyte assemblages: regulation in response to phosphorus supply and localization. J Phycol. 2010;46:93–101.
    CAS  Article  Google Scholar 

    51.
    Díaz-de-Quijano D, Felip M. A comparative study of fluorescence-labelled enzyme activity methods for assaying phosphatase activity in phytoplankton. A possible bias in the enzymatic pathway estimations. J Micro Meth. 2011;86:104–7.
    Article  CAS  Google Scholar 

    52.
    Ou L, Huang B, Lin L, Hong H, Zhang F, Chen Z. Phosphorus stress of phytoplankton in the Taiwan Strait determined by bulk and single-cell alkaline phosphatase activity assays. Mar Ecol Prog Ser. 2006;327:95–106.
    CAS  Article  Google Scholar 

    53.
    Huang B, Ou L, Wang X, Huo W, Li R, Hong H, et al. Alkaline phosphatase activity of phytoplankton in East China Sea coastal waters with frequent harmful algal bloom occurrences. Aquat Micro Ecol. 2007;49:195–206.
    Article  Google Scholar 

    54.
    Ivančić I, Pfannkuchen M, Godrijan J, Djakovac T, Pfannkuchen DM, Korlević M, et al. Alkaline phosphatase activity related to phosphorus stress of microphytoplankton in different trophic conditions. Prog Oceanogr. 2016;146:175–86.
    Article  Google Scholar 

    55.
    González-Gil S, Keafer B, Jovine JMR, Aguileral A, Lu S, Anderson DM. Detection and quantification of alkaline phosphatase in single cells of phosphorus-starved marine phytoplankton. Mar Ecol Prog Ser. 1998;164:21–35.
    Article  Google Scholar 

    56.
    Dyhrman ST, Ruttenberg KC. Presence and regulation of alkaline phosphatase activity in eukaryotic phytoplankton from the coastal ocean: Implications for dissolved organic phosphorus remineralization. Limnol Oceanogr. 2006;51. https://doi.org/10.4319/lo.2006.51.3.1381.

    57.
    Flynn K, Jones KJ, Flynn KJ. Comparisons among species of Alexandrium (Dinophyceae) grown in nitrogen- or phosphorus-limiting batch culture. Mar Biol. 1996;126:9–18.
    CAS  Article  Google Scholar 

    58.
    Perry MJ. Alkaline phosphatase activity in subtropical Central North Pacific waters using a sensitive fluorometric method. Mar Biol. 1972;15:113–9.
    CAS  Article  Google Scholar 

    59.
    Dyhrman ST, Palenik B. Phosphate stress in cultures and field populations of the dinoflagellate prorocentrum minimum detected by a single-cell alkaline phosphatase assay. Appl Environ Microbiol. 1999;65:3205–12.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Mulholland MR, Floge S, Carpenter EJ, Capone DG. Phosphorus dynamics in cultures and natural populations of Trichodesmium spp. Mar Ecol Prog Ser. 2002;239:45–55.
    CAS  Article  Google Scholar 

    61.
    Thomson B, Wenley J, Currie K, Hepburn C, Herndl GJ, Baltar F. Resolving the paradox: Continuous cell-free alkaline phosphatase activity despite high phosphate concentrations. Mar Chem. 2019;214:103671.
    CAS  Article  Google Scholar 

    62.
    Foster RA, Sztejrenszus S, Kuypers MMM. Measuring carbon and N2 fixation in field populations of colonial and free-living unicellular cyanobacteria using nanometer-scale secondary ion mass spectrometry. J Phycol. 2013;49:502–16.
    CAS  PubMed  Article  Google Scholar 

    63.
    Dyhrman ST, Palenik B. Characterization of ectoenzyme activity and phosphate-regulated proteins in the coccolithophorid Emiliania huxleyi. J Plankton Res. 2003;25:1215–25.
    CAS  Article  Google Scholar 

    64.
    Oh SJ, Yamammoto T, Kataoka Y, Matsuda O, Matsuyama Y, Katani Y. Utilization of dissolved organic phosphorus by the two toxic dinoflagellates, Alexandrium tamarense and Gymnodinium catenatum (Dinophyceae). Fish Sci. 2002;68:416–24.
    CAS  Article  Google Scholar 

    65.
    Jauzein C, Labry C, Youenou A, Quéré J, Delmas D, Collos Y. Growth and phosphorus uptake by the toxic dinoflagellate Alexandrium catenella (Dinophycea) in response to phosphate limitation. J Phycol. 2010;46:926–36.
    CAS  Article  Google Scholar 

    66.
    Elgavish A, Halmann M, Berman T. A comparative study of phosphorus utilization and storage in batch cultures of Peridinium cinctum, Pediastrum duplex and Cosmarium sp., from Lake Kinneret (Israel). Phycologia. 1982;21:47–54.
    CAS  Article  Google Scholar 

    67.
    Flynn K, Franco JM, Fernandez P, Reguera B, Zapata M, Wood G, et al. Changes in toxin content, biomass and pigments of the dinoflagellate Alexandrium minutum during nitrogen refeeding and growth into nitrogen or phosphorus stress. Mar Ecol Prog Ser. 1994;111:99–109.
    CAS  Article  Google Scholar 

    68.
    Ou L, Wang D, Huang B, Hong H, Qi Y, Lu S. Comparative study of phosphorus strategies of three typical harmful algae in Chinese coastal waters. J Plankton Res. 2008;30:1007–17.
    CAS  Article  Google Scholar 

    69.
    Droop MR. The nutrient status of algal cells in continuous culture. J Mar Biol Ass UK. 1974;54:825–55.
    CAS  Article  Google Scholar 

    70.
    Bechemin C, Grzebyk D, Hachame F, Hummert C, Maestrini S. Effect of different nitrogen/phosphorus nutrient ratios on the toxin content in Alexandrium minutum. Aquat Micro Ecol. 1990;20:157–65.
    Article  Google Scholar 

    71.
    Labry C, Erard–Le Denn E, Chapelle A, Fauchot J, Youenou A, Crassous MP, et al. Competition for phosphorus between two dinoflagellates: A toxic Alexandrium minutum and a non-toxic Heterocapsa triquetra. J Exp Mar Biol Ecol. 2008;358:124–35.
    CAS  Article  Google Scholar 

    72.
    Chapelle A, Labry C, Sourisseau M, Lebreton C, Youenou A, Crassous MP. Alexandrium minutum growth controlled by phosphorus An applied model. J Mar Syst. 2010;83:181–91.
    Article  Google Scholar 

    73.
    Sakshaug E, Granéli E, Elbrächter M, Kayser H. Chemical composition and alkaline phosphatase activity of nutrient-saturated and P-deficient cells of four marine dinoflagellates. J Exp Mar Biol Ecol. 1984;11:241–54.
    Article  Google Scholar 

    74.
    Lirdwitayaprasit T, Okaichi T, Montani S, Ochi T, Anderson DM. Changes in cell chemical con~position during the life cycle of Scrippsiella trochoidea (Dinophyceae). J Phycol. 1990;26:299–306.
    CAS  Article  Google Scholar 

    75.
    Qi H, Wang J, Wang Z. A comparative study of maximal quantum yield of photosystem II to determine nitrogen and phosphorus limitation on two marine algae. J Sea Res. 2013;80:1–11.
    Article  Google Scholar 

    76.
    Simon N, Cras AL, Foulon E, Lemée R. Diversity and evolution of marine phytoplankton. C R Biol. 2009;332:159–70.
    PubMed  Article  Google Scholar 

    77.
    Rengefors K, Kremp A, Reusch TBH, Wood AM. Genetic diversity and evolution in eukaryotic phytoplankton: revelations from population genetic studies. J Plankton Res. 2017;39:165–79.
    Google Scholar 

    78.
    Bendif EM, Nevado B, Wong ELY, Wong EL, Hagino K, Probert I, et al. Repeated species radiations in the recent evolution of the key marine phytoplankton lineage Gephyrocapsa. Nat Commun. 2019;10:4234.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    79.
    Thornton DCO. Individuals clones or groups? Phytoplankton behaviour and units of selection. Ethol Ecol Evol. 2002;14:165–73.
    Article  Google Scholar 

    80.
    Gerecht A, Romano G, Lanora A, d’Ippolito G, Cutignano A, Fontana A. Plasticity of Oxylipin metabolism among clones of the marine diatom Skeletonema marinoi (Bacillariophyceae). J Phycol. 2011;47:1050–6.
    CAS  PubMed  Article  Google Scholar 

    81.
    Lim PT, Leaw CP, Usup G, Kobiyama A, Koike K, Ogata T. Effects of light and temperature on growth, nitrate uptake, and toxin production of two tropical dinoflagellates: Alexandrium tamiyavanichii and Alexandrium minutum (Dinophyceae). J Phycol. 2006;42:786–99.
    CAS  Article  Google Scholar 

    82.
    Van Mooy BA, Fredricks HF, Pedler BE, Dyhrman ST, Karl DM, Koblížek M, et al. Phytoplankton in the ocean use non-phosphorus lipids in response to phosphorus scarcity. Nature. 2009;458:69–72.
    PubMed  Article  CAS  Google Scholar 

    83.
    Galbraith AD, Martiny AC. Simple mechanism for marine nutrient stoichiometry. Proc Natl Acad Sci USA. 2015;112:8199–204.
    CAS  PubMed  Article  Google Scholar 

    84.
    Berge T, Daugbjerg N, Hansen PJ. Isolation and cultivation of microalgae select for low growth rate and tolerance to high pH. Harmful Algae. 2012;20:101–10.
    CAS  Article  Google Scholar  More

  • in

    Symbiotic bacteria mediate volatile chemical signal synthesis in a large solitary mammal species

    Composition of chemical constituents and bacterial communities in AGS and feces indicates separate, unique odor profiles
    The gas chromatography–mass spectrometry analyses revealed that AGS volatiles of wild and captive pandas were comprised of a multicomponent blend of 30–50 chemical compounds, including fatty acids, aldehydes, ketones, aliphatic ethers, amides, aromatics, alcohols, steroids and squalene (Fig. 2a and Supplementary Table S2). These compounds are typical components of chemosignals across species due to their volatility, detectability and other characteristics facilitating chemoreception [3, 26, 32]. By contrast, feces contained mostly fatty acid ethyl ester, and a small number and quantity of fatty acids, amides, steroids and indole (Fig. 2b and Supplementary Table S3). Our results show that the relative abundance of steroids, aldehydes and fatty acids were remarkably higher in AGS than in feces (Fig. 3a), and the number of chemical components of aldehydes, fatty acids, and ketones in AGS was also significantly higher than found in feces (Fig. 3b). These results indicate that the chemical constituents of AGS are much better suited for chemosignaling than those from feces.
    Fig. 2: Representative ion chromatograms of samples in giant pandas.

    a Anogenital gland secretions (AGS). b Feces.

    Full size image

    Fig. 3: Differences in chemical compounds of anogenital gland secretions (AGS) and feces in giant pandas, and the differences in microbial communities, KEGG and contribution bacteria for lipid metabolism.

    a A heat map of the mean relative abundance of the chemical compounds. b A heat map of the number chemical components. Differences in the microbial communities as a function of providence (captive/wild) and source (feces/AGS) at the c phylum and d genus level. e PCoA clustering results of samples from different groups. f Hierarchical clustering analysis of the samples, clearly indicating two branches for AGS and fecal samples. g Six differentially represented pathways in lipid metabolism and the Linear discriminant analysis (LDA) score. h Prevalence of enzymes involved in lipid metabolism as a function of phylum and family in AGS of giant pandas. i The contribution of different bacteria at genus level to lipid metabolism. WPF: wild panda feces, CPF: captive panda feces, WPAG: wild panda AG, CPAG: captive panda AG.

    Full size image

    The composition of bacterial communities in AGS and feces was markedly different at the phylum (Fig. 3c) and genus levels (Fig. 3d), based on taxonomic classifications of predicted gene sequences. Principal Co-ordinates Analysis (PCoA) (Fig. 3e) and hierarchical clustering analyses (Fig. 3f) revealed cluster patterns based on provenance (captive/wild) and sample type (AGS/fecal). Notably, the microbiota composition of AGS from different individuals or living environments was more similar than were AGS and fecal samples from the same individuals. Actinobacteria (X2 = 26.33, P  More

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    Comparisons of fall armyworm haplotypes between the Galápagos Islands and mainland Ecuador indicate limited migration to and between islands

    1.
    Luginbill, P. The fall armyworm. USDA Tech. Bull. 34, 1–91 (1928).
    Google Scholar 
    2.
    Chandrasena, D. I. et al. Characterization of field-evolved resistance to Bacillus thuringiensis-derived Cry1F delta-endotoxin in Spodopterafrugiperda populations from Argentina. Pest Manag. Sci. 74, 746–754. https://doi.org/10.1002/ps.4776 (2018).
    CAS  Article  PubMed  Google Scholar 

    3.
    Farias, J. R. et al. Frequency of Cry1F resistance alleles in Spodoptera frugiperda (Lepidoptera: Noctuidae) in Brazil. Pest Manag. Sci. 72, 2295–2302. https://doi.org/10.1002/ps.4274 (2016).
    CAS  Article  PubMed  Google Scholar 

    4.
    Farias, J. R. et al. Field-evolved resistance to Cry1F maize by Spodoptera frugiperda (Lepidoptera: Noctuidae) in Brazil. Crop Prot. 64, 150–158 (2014).
    Article  ADS  Google Scholar 

    5.
    Huang, F. et al. Cry1F resistance in fall armyworm Spodoptera frugiperda: Single gene versus pyramided Bt maize. PLoS ONE 9, e112958. https://doi.org/10.1371/journal.pone.0112958 (2014).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    6.
    Storer, N. P. et al. Discovery and characterization of field resistance to Bt maize: Spodoptera frugiperda (Lepidoptera:Noctuidae) in Puerto Rico. J. Econ. Entomol. 103, 1031–1038. https://doi.org/10.1603/Ec10040 (2010).
    Article  PubMed  Google Scholar 

    7.
    Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr. Sci. India 115, 621–623 (2018).
    CAS  Article  Google Scholar 

    8.
    Nagoshi, R. N. Evidence that a major subpopulation of fall armyworm found in the Western Hemisphere is rare or absent in Africa, which may limit the range of crops at risk of infestation. PLoS ONE 14, e0208966. https://doi.org/10.1371/journal.pone.0208966 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    9.
    Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1421. https://doi.org/10.1038/s41598-020-58249-3 (2020).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    10.
    Shylesha, A. N. et al. Studies on new invasive pest Spodopterafrugiperda (J. E. Smith) (Lepidoptera: Noctuidae) and its natural enemies. J. Biol. Control 32, 145–151. https://doi.org/10.18311/jbc/2018/21707 (2018).
    Article  Google Scholar 

    11.
    DPIRD, G. o. W. A. Fall armyworm in Western Australia. http://www.agric.wa.gov.au/plant-biosecurity/fall-armyworm-western-australia (2020).

    12.
    Pair, S. D. & Sparks, A. N. in Long-range migration of moths of agronomic importance to the United States and Canada: Specific examples of occurrence and synoptic weather patterns conducive to migration (ESA Symposium, 1982). Vol. ARS-43 (ed A. N. Sparks) 25–33 (USDA Miscellaneous Publication, 1986).

    13.
    Mitchell, E. R. et al. Seasonal periodicity of fall armyworm, (Lepidoptera, Noctuidae) in the Caribbean basin and northward to Canada. J. Entomol. Sci. 26, 39–50 (1991).
    Article  Google Scholar 

    14.
    Nagoshi, R. N., Meagher, R. L. & Hay-Roe, M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecol. Evol. 2, 1458–1467 (2012).
    Article  Google Scholar 

    15.
    Westbrook, J. K. Noctuid migration in Texas within the nocturnal aeroecological boundary layer. Integr. Comp. Biol. 48, 99–106 (2008).
    Article  Google Scholar 

    16.
    16Danthanarayana, W. in Proceedings in life sciences (ed International Congress of Entomology) (Springer, Hamburg, 1986).

    17.
    Westbrook, J. K., Nagoshi, R. N., Meagher, R. L., Fleischer, S. J. & Jairam, S. Modeling seasonal migration of fall armyworm moths. Int. J. Biometeorol. 60, 255–267 (2016).
    CAS  Article  ADS  Google Scholar 

    18.
    Pashley, D. P. The current status of fall armyworm host strains. Fla Entomol. 71, 227–234 (1988).
    Article  Google Scholar 

    19.
    19Pashley, D. P. in Electrophoretic Studies on Agricultural Pests (eds H. D. Loxdale & J. der Hollander) 103–114 (Oxford University Press, Oxford, 1989).

    20.
    Juárez, M. L. et al. Host association of Spodoptera frugiperda (Lepidoptera: Noctuidae) corn and rice strains in Argentina, Brazil, and Paraguay. J. Econ. Entomol. 105, 573–582. https://doi.org/10.1603/Ec11184 (2012).
    Article  PubMed  Google Scholar 

    21.
    Murúa, M. G. et al. Demonstration using field collections that Argentina fall armyworm populations exhibit strain-specific host plant preferences. J. Econ. Entomol. 108, 2305–2315 (2015).
    Article  Google Scholar 

    22.
    Nagoshi, R. N., Silvie, P., Meagher, R. L., Lopez, J. & Machados, V. Identification and comparison of fall armyworm (Lepidoptera: Noctuidae) host strains in Brazil, Texas, and Florida. Ann. Entomol. Soc. Am. 100, 394–402 (2007).
    CAS  Article  Google Scholar 

    23.
    Levy, H. C., Garcia-Maruniak, A. & Maruniak, J. E. Strain identification of Spodoptera frugiperda (Lepidoptera: Noctuidae) insects and cell line: PCR-RFLP of cytochrome oxidase C subunit I gene. Fla Entomol. 85, 186–190 (2002).
    CAS  Article  Google Scholar 

    24.
    Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292. https://doi.org/10.1603/An09046 (2010).
    CAS  Article  Google Scholar 

    25.
    Nagoshi, R. N., Silvie, P. & Meagher, R. L. Comparison of haplotype frequencies differentiate fall armyworm (Lepidoptera: Noctuidae) corn-strain populations from Florida and Brazil. J. Econ. Entomol. 100, 954–961 (2007).
    Article  Google Scholar 

    26.
    Nagoshi, R. N., Meagher, R. L. & Jenkins, D. A. Puerto Rico fall armyworm has only limited interactions with those from Brazil or Texas but could have substantial exchanges with Florida populations. J. Econ. Entomol. 103, 360–367 (2010).
    Article  Google Scholar 

    27.
    Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE 14, e021775 (2019).
    Google Scholar 

    28.
    Nagoshi, R. N., Fleischer, S. & Meagher, R. L. Demonstration and quantification of restricted mating between fall armyworm host strains in field collections by SNP comparisons. J. Econ. Entomol. 110, 2568–2575 (2017).
    CAS  Article  Google Scholar 

    29.
    Nagoshi, R. N., Goergen, G., Du Plessis, H., van den Berg, J. & Meagher, R. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. UK 9, 8311 (2019).
    Article  ADS  Google Scholar 

    30.
    Nagoshi, R. N. & Meagher, R. L. Using intron sequence comparisons in the triose-phosphate isomerase gene to study the divergence of the fall armyworm host strains. Insect Mol. Biol. 25, 324–337. https://doi.org/10.1111/imb.12223 (2016).
    CAS  Article  PubMed  Google Scholar 

    31.
    Meagher, R. L. & Nagoshi, R. N. Population dynamics and occurrence of Spodoptera frugiperda host strains in southern Florida. Ecol. Entomol. 29, 614–620 (2004).
    Article  Google Scholar 

    32.
    Nagoshi, R. N. et al. Genetic characterization of fall armyworm (Lepidoptera: Noctuidae) host strains in Argentina. J. Econ. Entomol. 105, 418–428. https://doi.org/10.1603/Ec11332 (2012).
    CAS  Article  PubMed  Google Scholar 

    33.
    Meagher, R. L. & Nagoshi, R. N. Differential feeding of fall armyworm (Lepidoptera: Noctuidae) host strains on meridic and natural diets. Ann. Entomol. Soc. Am. 105, 462–470. https://doi.org/10.1603/An11158 (2012).
    Article  Google Scholar 

    34.
    Meagher, R. L., Nagoshi, R. N., Stuhl, C. & Mitchell, E. R. Larval development of fall armyworm (Lepidoptera: Noctuidae) on different cover crop plants. Fla Entomol. 87, 454–460 (2004).
    Article  Google Scholar 

    35.
    Prowell, D. P., McMichael, M. & Silvain, J. F. Multilocus genetic analysis of host use, introgression, and speciation in host strains of fall armyworm (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 97, 1034–1044 (2004).
    CAS  Article  Google Scholar 

    36.
    Nagoshi, R. N. et al. Genetic characterization of fall armyworm (Spodoptera frugiperda) in Ecuador and comparisons with regional populations identify likely migratory relationships. PLoS ONE 14, e0222332. https://doi.org/10.1371/journal.pone.0222332 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    37.
    Nagoshi, R. N. et al. Haplotype profile comparisons between Spodoptera frugiperda (Lepidoptera: Noctuidae) populations from Mexico with those from Puerto Rico, South America, and the United States and their implications to migratory behavior. J. Econ. Entomol. 108, 135–144 (2015).
    CAS  Article  Google Scholar 

    38.
    Nagoshi, R. N. et al. Fall armyworm migration across the Lesser Antilles and the potential for genetic exchanges between North and South American populations. PLoS ONE 12, e0171743 (2017).
    CAS  Article  Google Scholar 

    39.
    Peck, S. B., Heraty, J., Landry, B. & Sinclair, B. J. Introduced insect fauna of an oceanic archipelago: The Galápagos Islands, Ecuador. Am. Entomol. 44, 218–237. https://doi.org/10.1093/ae/44.4.218 (1998).
    Article  Google Scholar 

    40.
    Zapata, F. & Granja, M. M. Optimizing marine transport of food products to Galapagos: advances in the implementation plan. http://www.galapagos.org/wp-content/uploads/2012/04/trans1-optimizing-marine-transport.pdf (2009–2010).

    41.
    Toral-Granda, M. V. et al. Alien species pathways to the Galapagos Islands, Ecuador. PLoS ONE 12, e0184379. https://doi.org/10.1371/journal.pone.0184379 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    42.
    Nagoshi, R. N. et al. The genetic characterization of fall armyworm populations in Ecuador and its implications to migration and pest management in the northern regions of South America. PLoS ONE 15, e0236759. https://doi.org/10.1371/journal.pone.0236759 (2020).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
    Article  Google Scholar 

    44.
    Saitou, N. & Nei, M. The neighbor-joining method—A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).
    CAS  PubMed  Google Scholar 

    45.
    Clements, M. J., Kleinschmidt, C. E., Maragos, C. M., Pataky, J. K. & White, D. G. Evaluation of inoculation techniques for fusarium ear rot and fumonisin contamination of corn. Plant Dis. 87, 147–153 (2003).
    CAS  Article  Google Scholar 

    46.
    Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
    Article  Google Scholar 

    47.
    Murúa, G. M. et al. Fitness and mating compatibility of Spodoptera frugiperda (Lepidoptera: Noctuidae) populations from different host plant species and regions in Argentina. Ann. Entomol. Soc. Am. 101, 639–649 (2008).
    Article  Google Scholar 

    48.
    Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
    CAS  Article  Google Scholar 

    49.
    Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077. https://doi.org/10.1175/Bams-D-14-00110.1 (2015).
    Article  ADS  Google Scholar  More

  • in

    The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance

    1.
    Connell JH. Diversity in Tropical Rain Forests and Coral Reefs. Science. 1978;199:1302–10.
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Moreno-Mateos D, Barbier EB, Jones PC, Jones HP, Aronson J, López-López JA, et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat Commun. 2017;8:14163.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Rodil IF, Lohrer AM, Chiaroni LD, Hewitt JE, Thrush SF. Disturbance of sandflats by thin terrigenous sediment deposits: consequences for primary production and nutrient cycling. Ecol Appl. 2011;21:416–26.
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Carnell PE, Keough MJ. More severe disturbance regimes drive the shift of a kelp forest to a sea urchin barren in south-eastern Australia. Sci Rep. 2020;10:11272.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    5.
    McDowell NG, Michaletz ST, Bennett KE, Solander KC, Xu C, Maxwell RM, et al. Predicting Chronic Climate-Driven Disturbances and Their Mitigation. Trends Ecol Evol. 2018;33:15–27.
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Shade A, Peter H, Allison SD, Baho D, Berga M, Buergmann H, et al. Fundamentals of Microbial Community Resistance and Resilience. Front Microbiol. 2012;3:417.
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci. 2008;105:11512–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Shade A, Read JS, Welkie DG, Kratz TK, Wu CH, McMahon KD. Resistance, resilience and recovery: aquatic bacterial dynamics after water column disturbance: Bacterial community recovery after lake mixing. Environ Microbiol. 2011;13:2752–67.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Shade A, Read JS, Youngblut ND, Fierer N, Knight R, Kratz TK, et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 2012;6:2153–67.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci. 2011;108:4554–61.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Heinsen F-A, Knecht H, Neulinger SC, Schmitz RA, Knecht C, Kühbacher T, et al. Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin. Gut Microbes. 2015;6:243–54.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Fukuyama J, Rumker L, Sankaran K, Jeganathan P, Dethlefsen L, Relman DA, et al. Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment. PLOS Comput Biol. 2017;13:e1005706.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Subramanian S, Huq S, Yatsunenko T, Haque R, Mahfuz M, Alam MA, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature. 2014;510:417–21.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Antwis RE, Garcia G, Fidgett AL, Preziosi RF. Tagging Frogs with Passive Integrated Transponders Causes Disruption of the Cutaneous Bacterial Community and Proliferation of Opportunistic Fungi. Appl Environ Microbiol. 2014;80:4779–84.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Bates KA, Shelton JMG, Mercier VL, Hopkins KP, Harrison XA, Petrovan SO, et al. Captivity and Infection by the Fungal Pathogen Batrachochytrium salamandrivorans Perturb the Amphibian Skin Microbiome. Front Microbiol. 2019;10:1834.
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Gimblet C, Meisel JS, Loesche MA, Cole SD, Horwinski J, Novais FO, et al. Cutaneous Leishmaniasis Induces a Transmissible Dysbiotic Skin Microbiota that Promotes Skin Inflammation. Cell Host Microbe. 2017;22:13–24.e4.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Jani AJ, Briggs CJ. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc Natl Acad Sci. 2014;111:E5049–E5058.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22:850–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Longcore JE, Pessier AP, Nichols DK. Batrachochytrium Dendrobatidis gen. et sp. nov., a Chytrid Pathogenic to Amphibians. Mycologia. 1999;91:219–27.
    Article  Google Scholar 

    20.
    Berger L, Speare R, Daszak P, Green DE, Cunningham AA, Goggin CL, et al. Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America. Proc Natl Acad Sci. 1998;95:9031–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Crawford AJ, Lips KR, Bermingham E. Epidemic disease decimates amphibian abundance, species diversity, and evolutionary history in the highlands of central Panama. Proc Natl Acad Sci. 2010;107:13777–82.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. Proc Natl Acad Sci USA. 2006;103:3165–70.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Vredenburg VT, Knapp RA, Tunstall TS, Briggs CJ. Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci. 2010;107:9689–94.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Bletz MC, Loudon AH, Becker MH, Bell SC, Woodhams DC, Minbiole KPC, et al. Mitigating amphibian chytridiomycosis with bioaugmentation: characteristics of effective probiotics and strategies for their selection and use. Ecol Lett. 2013;16:807–20.
    PubMed  Article  PubMed Central  Google Scholar 

    25.
    Hardy BM, Pope KL, Piovia-Scott J, Brown RN, Foley JE. Itraconazole treatment reduces Batrachochytrium dendrobatidis prevalence and increases overwinter field survival in juvenile Cascades frogs. Dis Aquat Organ. 2015;112:243–50.
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    McMahon TA, Sears BF, Venesky MD, Bessler SM, Brown JM, Deutsch K, et al. Amphibians acquire resistance to live and dead fungus overcoming fungal immunosuppression. Nature. 2014;511:224–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Harris RN, Brucker RM, Walke JB, Becker MH, Schwantes CR, Flaherty DC, et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 2009;3:818–24.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Muletz CR, Myers JM, Domangue RJ, Herrick JB, Harris RN. Soil bioaugmentation with amphibian cutaneous bacteria protects amphibian hosts from infection by Batrachochytrium dendrobatidis. Biol Conserv. 2012;152:119–26.
    Article  Google Scholar 

    29.
    Becker MH, Harris RN, Minbiole KPC, Schwantes CR, Rollins-Smith LA, Reinert LK, et al. Towards a Better Understanding of the Use of Probiotics for Preventing Chytridiomycosis in Panamanian Golden Frogs. Ecohealth. 2011;8:501–6.
    PubMed  Article  PubMed Central  Google Scholar 

    30.
    Woodhams DC, Geiger CC, Reinert LK, Rollins-Smith LA, Lam B, Harris RN, et al. Treatment of amphibians infected with chytrid fungus: learning from failed trials with itraconazole, antimicrobial peptides, bacteria, and heat therapy. Dis Aquat Organ. 2012;98:11–25.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Belden LK, Hughey MC, Rebollar EA, Umile TP, Loftus SC, Burzynski EA, et al. Panamanian frog species host unique skin bacterial communities. Front Microbiol. 2015; 6:1171.

    32.
    Bletz MC, Goedbloed DJ, Sanchez E, Reinhardt T, Tebbe CC, Bhuju S, et al. Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat Commun. 2016;7:13699.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Jani AJ, Briggs CJ. Host and Aquatic Environment Shape the Amphibian Skin Microbiome but Effects on Downstream Resistance to the Pathogen Batrachochytrium dendrobatidis Are Variable. Front Microbiol. 2018;9:487.
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Kueneman JG, Parfrey LW, Woodhams DC, Archer HM, Knight R, McKenzie VJ. The amphibian skin-associated microbiome across species, space and life history stages. Mol Ecol. 2014;23:1238–50.
    PubMed  PubMed Central  Article  Google Scholar 

    35.
    Kueneman JG, Bletz MC, McKenzie VJ, Becker CG, Joseph MB, Abarca JG, et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat Ecol Evol. 2019;3:381–9.
    PubMed  Article  PubMed Central  Google Scholar 

    36.
    Küng D, Bigler L, Davis LR, Gratwicke B, Griffith E, Woodhams DC. Stability of Microbiota Facilitated by Host Immune Regulation: Informing Probiotic Strategies to Manage Amphibian Disease. PLoS ONE. 2014;9:e87101.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    37.
    McKenzie VJ, Bowers RM, Fierer N, Knight R, Lauber CL. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 2012;6:588–96.
    CAS  Article  Google Scholar 

    38.
    Prest TL, Kimball AK, Kueneman JG, McKenzie VJ. Host-associated bacterial community succession during amphibian development. Mol Ecol. 2018;27:1992–2006.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Rebollar EA, Hughey MC, Medina D, Harris RN, Ibáñez R, Belden LK. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 2016;10:1682–95.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Harrison XA, Price SJ, Hopkins K, Leung WTM, Sergeant C, Garner TWJ. Diversity-Stability Dynamics of the Amphibian Skin Microbiome and Susceptibility to a Lethal Viral Pathogen. Front Microbiol. 2019;10:2883.
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Jani AJ, Knapp RA, Briggs CJ. Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale. Proc R Soc B-Biol Sci. 2017;284:20170944.
    Article  Google Scholar 

    42.
    Walke JB, Becker MH, Loftus SC, House LL, Teotonio TL, Minbiole KPC, et al. Community Structure and Function of Amphibian Skin Microbes: an Experiment with Bullfrogs Exposed to a Chytrid Fungus. PLOS ONE. 2015;10:e0139848.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Knutie SA, Wilkinson CL, Kohl KD, Rohr JR. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat Commun. 2017;8:86.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Rachowicz LJ, Knapp RA, Morgan JA, Stice MJ, Vredenburg VT, Parker JM, et al. Emerging infectious disease as a proximate cause of amphibian mass mortality. Ecology. 2006;87:1671–83.
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Jones MEB, Paddock D, Bender L, Allen JL, Schrenzel MD, Pessier AP. Treatment of chytridiomycosis with reduced-dose itraconazole. Dis Aquat Organ. 2012;99:243–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Brannelly LA. Reduced Itraconazole Concentration and Durations Are Successful in Treating Batrachochytrium dendrobatidis Infection in Amphibians. JOVE-J Vis Exp. 2014;85:e51166.
    Google Scholar 

    47.
    Hyatt AD, Boyle DG, Olsen V, Boyle DB, Berger L, Obendorf D, et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis Aquat Organ. 2007;73:175–92.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Boyle DG, Boyle DB, Olsen V, Morgan JAT, Hyatt AD. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis Aquat Organ. 2004;60:141–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform. Appl Environ Microbiol. 2013;79:5112–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e1.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microbiol. 2009;75:7537–41.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    53.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–596.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8:1188.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    55.
    Arisdakessian C, Cleveland SB, Belcaid M. MetaFlow|mics: Scalable and Reproducible Nextflow Pipelines for the Analysis of Microbiome Marker Data. Pract Exp Adv Res Comput. 2020. Association for Computing Machinery, New York, NY, USA, pp 120–4.

    56.
    Lozupone C, Knight R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Appl Environ Microbiol. 2005;71:8228–35.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 

    58.
    Anderson MJ. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley statsref: statistics reference online. American Cancer Society;2017. p. 1–15.

    59.
    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Joseph MB, Knapp RA. Disease and climate effects on individuals jointly drive post-reintroduction population dynamics of an endangered amphibian. bioRxiv. 2018; 332114.

    61.
    SanMiguel AJ, Meisel JS, Horwinski J, Zheng Q, Bradley CW, Grice EA. Antiseptic Agents Elicit Short-Term, Personalized, and Body Site–Specific Shifts in Resident Skin Bacterial Communities. J Investig Dermatol. 2018;138:2234–43.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Volkman J. Sterols in microorganisms. Appl Microbiol Biotechnol. 2003;60:495–506.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Niño DF, Cauvi DM, De Maio A. Itraconazole, a Commonly Used Antifungal, Inhibits Fcγ Receptor–Mediated Phagocytosis: Alteration of Fcγ Receptor Glycosylation and Gene Expression. Shock. 2014;42:52.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    64.
    Tang C, Kamiya T, Liu Y, Kadoki M, Kakuta S, Oshima K, et al. Inhibition of Dectin-1 Signaling Ameliorates Colitis by Inducing Lactobacillus-Mediated Regulatory T Cell Expansion in the Intestine. Cell Host Microbe. 2015;18:183–97.
    CAS  Article  Google Scholar 

    65.
    Zuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, et al. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018;9:3663.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    66.
    Zaneveld JR, McMinds R, Vega Thurber R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:17121.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Wilber MQ, Jani AJ, Mihaljevic JR, Briggs CJ. Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome. Ecol Lett. 2019;23:88–98.
    PubMed  Article  PubMed Central  Google Scholar 

    68.
    Loudon AH, Woodhams DC, Parfrey LW, Archer H, Knight R, McKenzie V, et al. Microbial community dynamics and effect of environmental microbial reservoirs on red-backed salamanders (Plethodon cinereus). ISME J. 2013;8:830–40.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Santillan E, Constancias F, Wuertz S. Press Disturbance Alters Community Structure and Assembly Mechanisms of Bacterial Taxa and Functional Genes in Mesocosm-Scale Bioreactors. mSystems. 2020;5:e00471–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Rebollar EA, Gutiérrez-Preciado A, Noecker C, Eng A, Hughey MC, Medina D, et al. The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data. Front Microbiol. 2018;9:466.
    PubMed  Article  PubMed Central  Google Scholar 

    71.
    Mountain Yellow-legged Frog Interagency Technical Team. Interagency Conservation Strategy for Mountain Yellow-legged Frogs in the Sierra Nevada (Rana sierrae and Rana muscosa). Version 1. California Department of Fish and Wildlife, National Park Service, U.S. Fish and Wildlife Service, U.S. Forest Service; 2018. More

  • in

    Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes

    1.
    Warren J, Topping CJ, James P. A unifying evolutionary theory for the biomass–diversity–fertility relationship. Theor Ecol. 2009;2:119–26.
    Article  Google Scholar 
    2.
    Al-Mufti MM, Sydes CL, Furness SB, Grime JP, Band SR. A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. J Ecol. 1977;65:759–91.
    Article  Google Scholar 

    3.
    Grace JB, Anderson TM, Seabloom EW, Borer ET, Adler PB, Harpole WS, et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature. 2016;529:390–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Hooper DU, Chapin FS III, Ewel JJ, Hector A, Inchausti P, Lavorel S, et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr. 2005;75:3–35.
    Article  Google Scholar 

    5.
    Tilman D, Wedin D, Knops J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature. 1996;379:718–20.
    CAS  Article  Google Scholar 

    6.
    Grace JB. The factors controlling species density in herbaceous plant communities: an assessment. Perspect Plant Ecol. 1999;2:1–28.
    Article  Google Scholar 

    7.
    Grime JP. Plant strategies and vegetation processes. Chichester-New York-Brisbane-Toronto: John Wiley & Sons, Ltd.; 1979.

    8.
    Loreau M, Hector A. Partitioning selection and complementarity in biodiversity experiments. Nature. 2001;412:72–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Michalet R, Brooker RW, Cavieres LA, Kikvidze Z, Lortie CJ, Pugnaire FI, et al. Do biotic interactions shape both sides of the humped-back model of species richness in plant communities? Ecol Lett. 2006;9:767–73.
    PubMed  Article  PubMed Central  Google Scholar 

    10.
    Rajaniemi TK. Explaining productivity-diversity relationships in plants. Oikos. 2003;101:449–57.
    Article  Google Scholar 

    11.
    Wardle DA, Bonner KI, Barker GM, Yeates GW, Nicholson KS, Bardgett RD, et al. Plant remobals in perennial grassland: vegetation dynamics, decomposers, soil biodiversity, and ecosystem properties. Ecol Monogr. 1999;69:535–68.
    Article  Google Scholar 

    12.
    Fraser LH, Pither J, Jentsch A, Sternberg M, Zobel M, Askarizadeh D, et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science. 2015;349:302–5.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Adler PB, Seabloom EW, Borer ET, Hillebrand H, Hautier Y, Hector A, et al. Productivity is a poor predictor of plant species richness. Science. 2011;333:1750–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Bastida F, García C, Fierer N, Eldridge DJ, Bowker MA, Abades S, et al. Global ecological predictors of the soil priming effect. Nat Commun. 2019;10:3481.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Crowther TW, van den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 2017;15:579–90.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Global diversity and geography of soil fungi. Science. 2014;346:1256688.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    20.
    Bardgett RD, Wardle DA. Herbivore-mediated linkages between aboveground and belowground communities. Ecology. 2003;84:2258–68.
    Article  Google Scholar 

    21.
    Wardle DA. Communities and ecosystems linking the aboveground and belowground components (MPB-34). Princeton (New Jersey): Princeton University Press; 2002.

    22.
    Geyer KM, Barrett JE. Unimodal productivity–diversity relationships among bacterial communities in a simple polar soil ecosystem. Environ Microbiol. 2019;21:2523–32.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Wardle DA. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biol Rev. 1992;67:321–58.
    Article  Google Scholar 

    25.
    Geyer KM, Altrichter AE, Van Horn DJ, Takacs-Vesbach CD, Gooseff MN, Barrett JE. Environmental controls over bacterial communities in polar desert soils. Ecosphere. 2013;4:art127.
    Article  Google Scholar 

    26.
    Langenheder S, Prosser JI. Resource availability influences the diversity of a functional group of heterotrophic soil bacteria. Environ Microbiol. 2008;10:2245–56.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Hopkins FM, Torn MS, Trumbore SE. Warming accelerates decomposition of decades-old carbon in forest soils. Proc Natl Acad Sci USA. 2012;109:1753–61.
    Article  Google Scholar 

    28.
    Lal R. Soil carbon sequestration impacts on global climate change and food security. Science. 2004;304:1623–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Bertness MD, Callaway R. Positive interactions in communities. Trends Ecol Evol. 1994;9:191–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30.
    Hammarlund SP, Harcombe WR. Refining the stress gradient hypothesis in a microbial community. Proc Natl Acad Sci USA. 2019;116:15760.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Bastida F, Torres IF, Moreno JL, Baldrian P, Ondoño S, Ruiz-Navarro A, et al. The active microbial diversity drives ecosystem multifunctionality and is physiologically related to carbon availability in Mediterranean semi-arid soils. Mol Ecol. 2016;25:4660–73.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Wagg C, Bender SF, Widmer F, van der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci USA. 2014;111:5266–70.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Wieder WR, Allison SD, Davidson EA, Georgiou K, Hararuk O, He Y, et al. Explicitly representing soil microbial processes in Earth system models. Glob Biogeochem Cycles. 2015;29:1782–1800.
    CAS  Article  Google Scholar 

    35.
    Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Maestre FT, Quero J, Gotelli NJ, Escudero A, Ochoa V, Delgado-baquerizo M, et al. Plant species richness and ecosystem multifunctionality in global drylands. Science. 2012;335:214–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Kettler TA, Doran JW, Gilbert TL. Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America journal. vol. 65. Lincoln, Nebraska: 2001. p. 849–52. Journal Series no. 13277 of the Agric Res Div, Univ Neb, Linc, Ne.

    39.
    Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Buyer JS, Sasser M. High throughput phospholipid fatty acid analysis of soils. Appl Soil Ecol. 2012;61:127–30.
    Article  Google Scholar 

    41.
    Frostegård A, Bååth E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol Fertil Soils. 1996;22:59–65.
    Article  Google Scholar 

    42.
    Rinnan R, Bååth E. Differential utilization of carbon substrates by bacteria and fungi in tundra soil. Appl Environ Microbiol. 2009;75:3611–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Kaiser C, Frank A, Wild B, Koranda M, Richter A. Negligible contribution from roots to soil-borne phospholipid fatty acid fungal biomarkers 18:2ω6,9 and 18:1ω9. Soil Biol Biochem. 2010;42:1650–2.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Frostegård A, Tunlid A, Bååth E. Use and misuse of PLFA measurements in soils. Soil Biol Biochem. 2011;43:1621–5.
    Article  CAS  Google Scholar 

    45.
    Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Ramirez KS, Leff JW, Barberán A, Bates ST, Betley J, Crowther TW, et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc R Soc B. 2014;281:20141988.
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Breiman L. Random forests. Mach Learn. 2001;45:5–32.
    Article  Google Scholar 

    50.
    Delgado-Baquerizo M, Giaramida L, Reich PB, Khachane AN, Hamonts K, Edwards C, et al. Lack of functional redundancy in the relationship between microbial diversity and ecosystem functioning. J Ecol. 2016;104:936–46.
    Article  Google Scholar 

    51.
    Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2003.

    52.
    Grace JB. Structural equation modeling and natural systems. Cambridge: Cambridge University Press; 2006.

    53.
    Quinlan JR. Combining instance-based and model-based learning. In: Proceedings of the Tenth International Conference on International Conference on Machine Learning. Amherst, MA, USA: Morgan Kaufmann Publishers Inc.; 1993.

    54.
    Delgado-Baquerizo M. Obscure soil microbes and where to find them. ISME J. 2019;13:2120–4.
    PubMed  PubMed Central  Article  Google Scholar 

    55.
    Kuhn SW, Keefer C, Coulter N. Cubist: rule- and instance-based regression modeling. R package version 0.0.19; 2016.

    56.
    Bailey VL, Peacock AD, Smith JL, Bolton H. Relationships between soil microbial biomass determined by chloroform fumigation-extraction, substrate-induced respiration, and phospholipid fatty acid analysis. Soil Biol Biochem. 2002;34:1385–9.
    CAS  Article  Google Scholar 

    57.
    Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC. Global patterns in belowground communities. Ecol Lett. 2009;12:1238–49.
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    Xu X, Thornton PE, Post WM. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob Ecol Biogeogr. 2013;22:737–49.
    Article  Google Scholar 

    59.
    Six J, Frey SD, Thiet RK, Batten KM. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J. 2006;70:555–69.
    CAS  Article  Google Scholar 

    60.
    Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;348:1–11.
    Google Scholar 

    61.
    Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. Proc Natl Acad Sci USA. 2006;103:626–31.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci USA. 2015;112:15684–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Delgado-Baquerizo M, Eldridge DJ. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems. 2019;22:1220–31.
    Article  Google Scholar 

    65.
    Větrovský T, Kohout P, Kopecký M, Machac A, Man M, Bahnmann BD, et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat Commun. 2019;10:5142.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    66.
    Gaston KJ. Global patterns in biodiversity. Nature. 2000;405:220–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Srivastava DS, Lawton JH. Why more productive sites have more species: an experimental test of theory using tree-hole communities. Am Naturalist. 1998;152:510–29.
    CAS  Article  Google Scholar 

    68.
    Storch D, Bohdalková E, Okie J. The more-individuals hypothesis revisited: the role of community abundance in species richness regulation and the productivity–diversity relationship. Ecol Lett. 2018;21:920–37.
    PubMed  Article  PubMed Central  Google Scholar 

    69.
    Paquette A, Messier C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob Ecol Biogeogr. 2011;20:170–80.
    Article  Google Scholar 

    70.
    Dorrepaal E, Toet S, van Logtestijn RSP, Swart E, van de Weg MJ, Callaghan TV, et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature. 2009;460:616–9.
    CAS  Article  Google Scholar 

    71.
    Melillo JM, Butler S, Johnson J, Mohan J, Steudler P, Lux H, et al. Soil warming, carbon–nitrogen interactions, and forest carbon budgets. Proc Natl Acad Sci USA. 2011;108:9508–12.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    72.
    Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, et al. Quantifying global soil carbon losses in response to warming. Nature. 2016;540:104–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    73.
    Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418:671–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    74.
    Navarrete AA, Tsai SM, Mendes LW, Faust K, de Hollander M, Cassman NA, et al. Soil microbiome responses to the short-term effects of Amazonian deforestation. Mol Ecol. 2015;24:2433–48.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    75.
    Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EdC, Paula FS, et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA. 2013;110:988–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    76.
    Bastida F, García C, von Bergen M, Moreno JL, Richnow HH, Jehmlich N. Deforestation fosters bacterial diversity and the cyanobacterial community responsible for carbon fixation processes under semiarid climate: a metaproteomics study. Appl Soil Ecol. 2015;93:65–7.
    Article  Google Scholar 

    77.
    Huang J, Yu H, Guan X, Wang G, Guo R. Accelerated dryland expansion under climate change. Nat Clim Change. 2016;6:166–71.
    Article  Google Scholar 

    78.
    Maron PA, Sarr A, Kaisermann A, Léveque J, Mathieu O, Guigue J, et al. High microbial diversity promotes soil ecosystem functioning. Appl Environ Microbiol. 2018;84:e02738–17.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    79.
    Chen C, Chen HYH, Chen X, Huang Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat Commun. 2019;10:1332.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    80.
    Delgado-Baquerizo M, Grinyer J, Reich PB, Singh BK. Relative importance of soil properties and microbial community for soil functionality: insights from a microbial swap experiment. Funct Ecol. 2016;30:1862–73.
    Article  Google Scholar 

    81.
    Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006;15:259–63.
    Article  Google Scholar  More