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Fairy circles (FCs) are remarkably circular and regular vegetation patterns covering an area of thousands of square kilometres in hyper-arid grasslands in Southern Africa (e.g. Namibia)1,2,3. They are typically made up of perennial tufts of Stipagrostis grass growing around barren circular patches roughly 2–10 m in diameter, with annual or perennial grasses in the area between circles, the matrix1,2. The edges of FCs are about 5–10 m apart and when dense they have a regular hexagonal arrangement3. Similar vegetation rings also occur in arid lands in Australia, Israel and elsewhere1,2,3,4,5,6,7. The processes leading to the formation of FCs have been the subject of debate for several decades8,9,10,11,12,13.
Aspects of FCs that require explanation include how and why individual circles form, how they persist and how the similar size and regular pattern is established at the landscape scale. The bare centres of individual FCs persist in situ almost permanently, although the peripheral plants delimiting the circles are much shorter-lived. Van Rooyen et al.8 noted no change in the position of five marked FCs over 22 years, despite intervening droughts8. Tschinkel14 analysed pairs of aerial photographs taken 4 years apart, and on the basis of how many FC positions were unchanged, died or emerged, estimated that average sized FCs persist in situ for an average of 75 years, implying some circles remain in situ for a century14. Similarly, using aerial photographs, Juergens2 noted a high survival (97%) of FCs in situ over a 50-year period (=0.06% pa mortality), implying an age of millennia for some FCs2.
Many alternative hypotheses have been proposed for FC spatial and temporal patterns, but without agreement. The first hypothesis is based on ecosystem engineering by termites that remove plants from the centres of circles2, facilitating localized underground water accumulation in circle centres. This moisture maintains the termites and the band of perennial grass on which termites feed year-round. The spatial patterning of the FCs is considered to result from competition between termite colonies2. However, the poor correlation of FCs with the presence of specific termites is an important concern with this hypothesis13.
The second hypothesis is based on the clonal mode of growth of individuals of many arid-land species that create vegetation rings4,6,7,15. For example, rings are formed by one of the Namibian FC species, Stipagrostis ciliata in the Negev desert. Here individual plants of this species send out underground rhizomes, which, with increasing age, results in a ring of ramets (i.e. sprouts from the same clonal colony or genet) around a barren central patch, which forms as the plant centre dies4. Such vegetation rings form and enlarge centrifugally due to competition between ramets, and as this process continues over time new ramets establish successfully only towards the periphery4,7. Globally, all of the many plant species that form circles do so by this type of clonal growth15. As the pattern of FCs is virtually fixed in situ for centuries, this suggests that the plants that indicate the pattern may also be long-lived. Individual clonal plants can be extremely long-lived15,16, which could then match longevities between the plants and the FCs they delimit. The clonality hypothesis could thus explain how circular shapes form (by self-thinning of ramets spreading from source plant), why bare centres occur (resource depletion and source plant death) and how they can persist over long periods of time (by continuous production of short-lived ramets). However, clonality has been disputed8 as an explanation for FCs for two reasons. Firstly, van Rooyen et al. suggested that the FCs referred to by Danin and Orshan are considerably smaller (about 2 m) than most FCs in Namibia4,8. Although this is correct that FCs tend to be much larger in Namibia, the mean size of FCs can be as small as 2.5 m in some areas2. Secondly, van Rooyen and colleagues suggested that clonality cannot explain why FC centres are bare8. Bare centres are now well known in rings and are commonly explained as being due to inter-ramet competition and resource depletion7.
The third hypothesis for FC spatial patterns is that it emerges through vegetation self-organization (the VSO hypothesis)1,5,12,17,18. Grasses in the peripheral band outcompete grasses in the FC centre and keep it bare and moist at depth1. The plants in the peripheral bands also compete with the matrix grasses and with the peripheral bands on adjacent FCs to maintain the regular pattern1. Mathematical vegetation models based on partial differential equations represent the theoretical basis of the VSO hypothesis17,18. These partial differential equations do not operate at the scale of individual-plant interactions, but at the level of local processes (largely rates of lateral water movement due to plant evapo-transpiration) in comparison to rates of lateral biomass spread. For these current VSO models, lateral water flow needs to be about 100 times faster than lateral biomass spread18. Thus, the mode and rate of how biomass spreads, whether via clonal growth or seed dispersal and population growth18, has an impact on the viability of vegetation-patterning models. In the absence of such information for FCs, the most recent VSO modelling study18 assumed a rate of biomass dispersal (1.2 m2 yr−1) derived from the Canadian woodlands19, for clonal spread or seed dispersal. These literature values may be unrealistic for FCs. For example, 1.2 m2 yr−1 is likely to be too high for clonal growth in the arid circumstances in which FCs occur. Stipagrostis individual plants across the landscape are typically only 0.005–0.13 m2 in canopy area with a mean area of 0.05 m2 (ref. 20). Similarly, clonal spread in other systems can also be lower than 1.2 m2 yr−1 by orders of magnitude (ref. 19). Alternatively, if Stipagrostis plants are not clones, their highly awned seeds will disperse much further than 1.2 m2 yr−1. Finally, if the assumed 1.2 m2 yr−1 biomass spread18 is due to the total growth of new recruits per parent individual, then this implies unrealistically high population growth rates ( >20 new recruits, each with 0.05 m2 in canopy volume20 required per parent individual). Getzin et al.3 acknowledge that in the context of their VSO model it needs to be further investigated whether grass tufts experience a central dieback due to self‐thinning, i.e. the role of clonality33. Thus, clonality may be relevant to some VSO models.
Juergens has argued that there is a spatial mismatch in the root length and inter-FC distances10 and therefore that FCs cannot directly interact with each other to produce the regular spatial pattern. An extreme example of this mismatch is a study by Ravi et al. reporting that the roots of peripheral plants in FCs had a mean length of 5.9 cm, which is more than two orders of magnitude shorter than inter-circle distances (10–20 m)12. These short root lengths would make competition for water and other resources over long distances crucial for explaining FC patterning1. Most recently, Ravi et al.12 rejected the termite hypothesis due to an absence of termites and partially invoked clonal dynamics as well as the VSO to explain their FC patterning12.
In summary, there are critical issues with all the hypotheses explaining the formation of FCs and the role of clonality appears to be relevant to two of the hypotheses. Here, we use ddRAD-seq as a genetic test of clonality of peripheral grasses. Our analysis indicates that most individual grasses surrounding FCs are genetically distinct and does not support the clonality hypothesis. More

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Site description
The study site is a typical semiarid grassland representative of the Eurasian steppe17, located in the Xilin River Basin, Inner Mongolia Autonomous Region of China, close to the Inner Mongolia Grassland Ecosystem Research Station (IMGERS, 43°38′ N, 116°42′ E). Mean annual precipitation is 346 mm, with 60–80% of precipitation falling in the growing season (May to September). Mean annual temperature is 0.3 °C, with mean monthly temperatures ranging from −21.6 °C in January to 19.0 °C in July4. The topography at our experimental site consists of two landscape units (i.e. flat block and sloped block), with elevation ranging from 1200 to 1280 m above sea level, and slopes less than 5°18,19. The soil is classified as dark chestnut (Calcic Chernozem, ISSS Working Group RB, 1998) derived from aeolian sediments18,20. The soil substrate is dominated by sandy loam and loamy sand with more than 50% being fine sand and silt21. At the beginning of the experiment, soil organic carbon and total nitrogen contents were higher in the flat block than in the sloped block (Table 1). Plant species richness and above-ground biomass were also greater in the flat block than in the sloped block, although species composition in terms of relative biomass of common species did not differ between the two systems (Table 1). Leymus chinensis (perennial rhizomatous grass) and Stipa grandis (perennial bunchgrass) are the dominant species in the study area, together accounting for more than 70% of community aboveground biomass. Other dominant species include Cleistogenes squarrosa, Agropyron cristatum, Achnatherum sibiricum, and Carex korshinskyi.
Table 1 Soil and vegetation characteristics in the flat and sloped blocks prior to grazing and mowing interventions.
Full size table

Study design
The experimental area was used for moderate sheep grazing (1.5–3 ewes ha−1 year−1) by local herdsmen until 2003. Afterwards, grass swards recovered for two years before the experiment started20,22. At the end of the growing season in 2004, prior to beginning the experiment, swards in the entire area were cut to 3–5 cm in stubble height23. The experiment was established in June 2005 with split plots in a randomized complete block design (Fig. 1). The study area included two blocks (i.e., flat and sloped blocks), with each block further divided into seven plots. We included flat and sloped blocks because our project was designed to assess the impacts of grazing at spatial scales that are both relevant to land management and that can capture ecosystem and landscape-scale effects of grazing24. It is unrealistic to conduct such a study in an area with no variation in topography. Grazing intensity was randomly assigned to the plots, and each plot was divided into two subplots. The grazing or mowing management regime was randomly assigned into each subplot23. In the grazing regime, there were seven levels of grazing intensity (GI: 0, 1.5, 3.0, 4.5, 6.0, 7.5 and 9.0 sheep ha−1), and sheep grazed in the subplots continuously from June to September each year25. The ungrazed plots (0 sheep ha−1) had no sheep grazing for 12 years. Each subplot was 2 ha, except the subplot with 1.5 sheep ha−1, which was enlarged to 4 ha to ensure a minimum herd of six sheep per subplot. In the mowing regime, mowing was done once a year in the middle of August. Plant and soil microbial community data was collected in late July and early August 2017, after 12 years of grazing and mowing treatments.
Fig. 1

Illustration of the grazing experiment design. G: grazing regime, M: mowing regime.

Full size image

Plant community surveys
For each subplot, we randomly laid out ten 1 m × 1 m quadrats at least five meters from the edge of each plot to avoid edge effects. In each quadrat, plant species were identified, and the abundance of each species was counted by bunches (bunchgrasses) or stems (rhizomatous grasses). For each species, five individuals were randomly chosen to measure plant height and the average height of all species was used as plant canopy height. Plant canopy coverage was measured visually.
Soil sampling
For each quadrat, three soil cores (3 cm diameter, 10 cm depth) were collected, and soil was passed through a 2 mm sieve to form one composite soil sample per quadrat. Sieved soil was then divided into three subsamples. One subsample was air-dried for the analysis of soil pH, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). The second fresh subsample was used for the analysis of microbial community structure and microbial biomass. The third subsample was stored at -20 °C prior to being used for microbial sequencing analysis.
Soil physical and chemical properties
To evaluate soil compaction, we measured soil hardness by using a Yamanaka-style soil hardness tester (Fujiwara Scientific Co., Japan). Soil moisture content was measured by using 10 g of moist soil that was oven-dried at 105 °C for 24 h. Soil pH was measured in a 1:2.5 soil:water suspension using a pH meter (FE20-FiveEasy, Mettler-Toledo, Switzerland).
We measured SOC content with the Walkley-black method, soil TN content by the micro-Kjeldahl digestion, followed by colorimetric determination with a 2300 Kjeltec Analyzer Unit, and soil TP content was by the H2SO4-HClO4 fusion method using a 6505 UV spectrophotometer26.
Soil microbial community structure
Microbial community structure was assessed using phospholipid fatty acids (PLFAs), as described by Bossio and Scow27. First, lipids were extracted from 10 g of fresh soil using a buffer (CHCL3:CH3OH:K2HPO4 = 1:2:0.8, v:v:v). Second, the fatty acid methyl esters (FAMEs) were separated, quantified and identified using a gas chromatograph system (Agilent 7890, Santa Clara, USA) and a MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, USA). Peak areas were converted to nmol g−1 dry soil using the internal standard, methylnon-adecanoate (C19:0). Third, the specific microbial groups were identified according to their representative markers. Specifically, G+ bacteria correspond to iso-, anteiso- and 10Me-branched PLFAs; G- bacteria correspond to monounsaturated and cyclopropyl PLFAs; arbuscular mycorrhizal fungi (AMF) use 16:1ω5c as representative marker; saprotrophic fungi (SF) use 18:1ω9c, 18:2ω6c and 18:3ω6c as representative markers28,29,30. The 12:0, 14:0, 15:0, 16:0, 17:0, 18:0 PLFAs were general markers present in all microorganisms30,31. Bacterial PLFAs included G+ and G− bacteria PLFAs. Fungal PLFAs included arbuscular mycorrhizal and saprotrophic fungi PLFAs. Total microbial PLFAs were the sum of bacterial, fungal, and general PLFAs.
Soil microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) were measured using the chloroform-extraction method32,33. For MBC and MBN, two fresh soil samples were used for the analysis. One sample was placed in a chloroform steam bath for 24 h and another sample was kept non-fumigated. Then, organic C and total N were extracted by shaking two soil samples in 0.5 M K2SO4 for 1 h and filtering through a Whatman No. 1 filter paper (9 cm in diameter). The filtered extracts were measured with a total organic carbon (TOC) analyzer (Elementar vario TOC, Hanau, Germany). Microbial biomass P was measured using a similar method as for MBC and MBN except that P was extracted by 0.5 M NaHCO3 and then measured with a UV Spectrometer (6505 spectrometer, Jenway, Stone, UK).
DNA extraction and sequencing
We mixed ten soil samples of each plot to form one composite sample for DNA extraction and sequencing. Total genomic DNA was extracted from 0.5 g soil using a FastDNA Spin kit (MP Biomedical, Santa Ana, California, USA). The DNA quality was checked by 1% agarose gel electrophoresis and quantity was determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington). Bacterial 16 S rRNA genes were amplified with PCR primers 338 F (5′- ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′). Fungal internal transcribed spacer (ITS) rRNA genes were amplified with PCR primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′)34,35. The resulting PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA). Purified amplicons were pooled in equimolar concentrations and paired-end sequenced for high-throughput 16 S rRNA or ITS rRNA gene sequencing on an Illumina Hiseq. 2500 platform (Illumina Inc., USA) according to the standard protocols by Novogene Technology Co., Ltd. Operational taxonomic units (OTUs) were clustered with 97% similarity cut-off using UPARSE (version 7.1 https://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. Silva and Unite databases were used as references for bacteria and fungi, respectively34,35. More

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Impact of Clothianidin on melanization and clotting
Insects: honey bees used in this study were from Apis mellifera ligustica colonies, maintained in the experimental apiary of the University of Napoli “Federico II”, Department of Agricultural Sciences. Larvae and newly emerged bees used in all the experiments were obtained from brood frames taken from the experimental hives and kept in an incubator at 34 °C, 80% relative humidity for 12 h.
Implantation experiment: 3rd instar larvae were first fed with 0.05, 0.01 ppm and no Clothianidin, while adults were treated with 20.0, 10.0, 5.0, 2.0 ng/bee and no Clothianidin, as already published4 (5 individuals for each treatment for both larvae and adults). In order to evaluate the encapsulation and melanization index12 a piece of transparent, nylon fluorocarbon coated fishing line (Ø = 0.08 mm; Asso Fishing Line), sterilized under UV light for 24 h, was inserted into the hemocelic cavity on 4th body segment of 5th instar larvae and into the haemocoelic cavity of adults through the membrane between the 3rd and 4th abdominal tergite. After 24 h, the implants were removed and subjected to image analysis, using GIMP version 2.8 (GNU Image Manipulation Program; www.gimp.org). In adult bees the clotting index was also analyzed by evaluating, after 24 h, the healing of a wound generated by piercing the honeybee integument inter-membrane between the 3rd and 4th abdominal tergite, using a sterile entomological needle. The rest of body was immediately stored at –80 °C for the subsequent molecular analysis. The experiment was repeated 3 times.
Immune genes expression and DWV quantification: in order to assess the relative expression of Amel102 and Dorsal 1A as affected by Clothianidin treatment, two groups of 4th instar larvae (n = 100 per group) received 0.01 ppm of a Clothianidin-treated diet or a clean diet, respectively, as detailed below. After 24 and 72 h from feeding, 15 larvae for each experimental group were sampled and stored at –80 °C for subsequent analysis.
RNA extraction, DWV quantification and relative gene expression data analysis were performed according to already published protocols12. Briefly, total RNA was isolated from individual honey bees using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. The quantity and the quality of total RNA were assessed using Varioskan Flash spectrophotometer (Thermo Fisher Scientific).
Differential relative expression of Amel102 and Dorsal 1A was measured by one-step qRT-PCR, using the Power SYBR Green RNA-to-Ct 1-Step Kit (Applied Biosystems, Carlsbad, CA, USA), according to the manufacturer’s instructions. Each reaction was prepared in 20 μL and contained 10 μL qRT-PCR mix 2X, 100 nM of forward and reverse primers, 0.16 μL of 125X RT enzyme mix, DEPC treated water and 50 ng of total RNA. All samples were analyzed in duplicate on a Step One Real Time PCR System (Applied Biosystems). Two reference genes, β-actin and rps5, were used as endogenous control for RNA loading. Relative gene expression data were analyzed using the ∆∆Ct method.
The quantification of DWV genome copies was performed using the Power SYBR Green RNA-to-Ct 1-Step Kit (Applied Biosystems) as described above. Titers of DWV were determined by relating the Ct values of unknown samples to an established standard curve. The standard curve was established by plotting the logarithm of seven 10-fold dilutions of a starting solution containing 21.9 ng of plasmid DNA pCR II-TOPO (TOPO-TA cloning) with a DWV insert (from 21.9 ng to 21.9 fg), against the corresponding Ct value as the average of three repetitions. The PCR efficiency (E = 107.5%) was calculated based on the slope and coefficient of correlation (R2) of the standard curve, according to the following formula: E = 10(−1/slope) − 1 (slope = −3.155, y-intercept = 41.84, R2 = 0.999). All primers used are shown in Supplementary Table 1.
Impact of Clothianidin on the reproduction of Varroa destructor
The artificial diet used for feeding 4th instar larvae (L4) contained D-glucose (9%), D-fructose (9%), yeast extract (2%) and royal jelly (50%)37. Fresh royal jelly was bought from a local supplier. Chemical analysis of royal jelly carried out by the supplier revealed no acaricides, pesticides or antibiotic contaminants. Before use, royal jelly was treated with γ-rays (25 kGy) to eliminate any possible microbial contamination.
A group of larvae received 0.01 ppm of Clothianidin-treated diet, while another group of larvae (control) received a clean diet. To prepare 100 g of Clothianidin-treated diet, 5 mg of Clothianidin were dissolved into 500 μL of acetone (solution A); then, 100 μL of solution A were diluted in 9900 μL of acetone (solution B); finally, 10 μL of solution B were dissolved in 990 μL of deionised water, which was used for the preparation of the diet.
After preparing the diet, 3–4 combs containing larvae of different ages were selected from the experimental apiary of the University of Udine, Italy. Fourth instar larvae (L4) were manually collected and transferred into sterile Petri dishes (Ø = 9 cm) containing 15 g of clean or Clothianidin-treated diet. Each Petri dish hosted 15–20 L4, for a total of 80–100 L4 per treatment per replication. Larvae were maintained in Petri dishes for 24 h under controlled conditions (35 °C, 90% R.H., dark).
Mites were collected from brood cells capped in the preceding 15 h. To this aim, in the afternoon of the day preceding the experiment, when the artificial feeding of larvae was carried out, the capped brood cells of several combs were marked. The following morning, the combs were transferred to the lab and the unmarked cells, that had been capped overnight, were manually unsealed. The combs were then placed in an incubator at 35 °C and 75% R.H., where larvae and mites spontaneously emerged.
In the meantime, the larvae fed with Clothianidin (or not) that had reached the 5th instar (L5) were cleaned from the larval food and transferred into gelatin capsules (Agar Scientific ltd., Ø = 6.5 mm) with 1 mite38. Infested bees were maintained in a climatic chamber under controlled conditions (35 °C, 75% R.H.) for 12 days until eclosion. From 58 to 77 L5 per experimental group per replicate were infested, for a total of 204 and 210 individuals per experimental group.
Daily, dead larvae were removed and counted. Upon eclosion, mite mortality and reproduction (i.e. fertility and fecundity) were measured by inspecting, in total, 111 and 120 mite infested honey bees fed or not with Clothianidin during the larval stage, respectively. Once separated from the infesting mite, 28 and 27 newly emerged adult bees in total, fed or not with Clothianidin during the larval stage, respectively, were stored at –80 °C for subsequent analysis aiming at assessing DWV load. The experiment was replicated 3 times.
Modeling of Varroa population as affected by Clothianidin
In order to test whether the effect of Clothianidin on Varroa reproduction could account for the higher mite infestation observed in colonies exposed to Clothianidin, under field conditions, we compared the data resulting from a simplified discrete time model of Varroa population with those obtained from the literature13.
At each time point, our simplified discrete time model calculates Varroa population as follows:

Varroa mites =Varroa mites + Varroa born − Varroa dead

Varroa born = (Varroa mites*proportion of mites in brood cells*proportion of mites producing viable offspring)/length of reproducing phase

Varroa dead = (Varroa mites*proportion of mites in brood cells*mortality of mites in brood cells + Varroa mites*(1 − proportion of mites in brood cells)*mortality of phoretic mites)/length of reproducing phase

Parameters were derived from published studies20,39, as detailed in the Supplementary Data File. The proportion of treated mites producing viable offspring was calculated according to the results of our experiment (i.e., proportion of treated mites producing viable offspring = proportion of control mites producing viable offspring +23%). Since, the model allowed to estimate the size of Varroa population in treated and control colonies, whereas field studies reported the number of mites on bottom boards13, these latter data were converted into colony infestation according to a standard coefficient derived from literature40.
The model above was used to follow the number of mites in two experimental groups (treated and control) for the duration of the field experiment that was used as a reference. More details can be found in the Supplementary Data file.
Statistical analysis
The statistical tests that were used to assess significance and the relevant data are reported along the corresponding results in the Supplementary Data file. Briefly, data about melanization, encapsulation, clotting, DWV infection level, and gene expression were analyzed by means of non-parametric methods (i.e., Mann–Whitney U tests in case of two samples and Kruskal–Wallis for more), the proportion of reproducing mites in different experimental groups was tested using the Mantel–Haenszel test, clotting in adult bees exposed to different doses of Clothianidin was tested with Spearman’s correlation. If necessary, probabilities were adjusted using the Bonferroni correction. Tests were performed with Excel (version 14.3.5).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Affiliations

International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
Shigenori Fujikawa, Roman Selyanchyn & Toyoki Kunitake

NanoMembrane Technologies, Inc., 4-1, Kyudai-Shimachi, Nishi-Ku, Fukuoka, 819-0388, Japan
Shigenori Fujikawa & Toyoki Kunitake

Department of Chemistry and Biochemistry, Center for Molecular Systems (CMS), Kyushu University, 744 Motooka, Nishiku, Fukuoka, 819-0395, Japan
Shigenori Fujikawa

Authors
Shigenori Fujikawa

Roman Selyanchyn

Toyoki Kunitake

Corresponding author
Correspondence to Shigenori Fujikawa. More