Philippa Kaur

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International Waterbird Census (IWC) data suggest 309,000 Scaup were wintering in North-West Europe in 1988–1991, compared with 192,300 in 2015–2018, indicating that the number of Scaup in this flyway has declined by 38.1% over 31 years (equivalent to a 30.3% decline over three generations, given a Scaup generation length of 8.2 years). Such a rate of decrease qualifies this population as vulnerable (VU) according to criterion A2(c) of the International Union for Conservation of Nature24. Thus, our results confirm the recent attribution of Scaup as a VU on the European Red List18. We suggest that the 1% threshold for the North-West Europe population of the Scaup should be revised to 1900.
In addition to the overall decline in abundance, we also show that changes in winter temperature on the eastern and northern edges of the wintering range potentially explain the observed dramatic shift in winter distribution closer to the breeding grounds. Climate change appears to have opened up more wintering sites to Scaup, especially in the more northern and eastern areas where reductions in winter ice cover have made previous staging sites increasingly accessible in winter. This might be expected to have a positive effect on the population, given that Scaup have more potential wintering sites to choose between and that they face a diminished risk from mass starvation because of the reduced probability of unexpected ice cover of potential feeding areas25. However, the ultimate causes of shifts in wintering distribution remain unknown and could equally relate to deterioration of food quality in southern and western wintering grounds. At Lake IJsselmeer, the annual changes in the large numbers of wintering Scaup there in the 1980s and 1990s were explained by fluctuations in the abundance of their main prey, Zebra Mussel Dreissena polymorpha26. The decline in Zebra Mussels in the IJsselmeer lake and its replacement by Quagga Mussels Dreissena rostriformis bugensis27 resulted in a deterioration in the quality of food resources at the site. These are likely contributory reasons to explain the shift in the centre of gravity of the Scaup wintering grounds to Poland and eastern Germany, although we lack data to determine the magnitude of this effect (Fig. 4, Unit#3). This area now constitutes the most important wintering area for this population, although the detection of Quagga Mussels in this region in 201428 represents a potential threat to the quality of this important wintering ground.
Assuming that some of the birds remain to winter along the migration route on sites formerly only used as stopovers, we can retrospectively infer the migration route of the Scaup population breeding in northern Russia and Fennoscandia (Fig. 1). It would appear that after birds reach the Baltic, they stop in Estonia before traversing the Baltic south-west to Gotland, migrating along the southern coast of Sweden and onwards to the main wintering area in Danish, German and Polish Baltic waters (Unit#3). Some Scaup continue west to reach Unit#2 in the Netherlands, and small numbers continue to reach France and the UK. The small population breeding in Iceland likely winter exclusively in the UK and Ireland, where fewer of the Russian/Fennoscandia population reach in recent winters. The Iceland breeding birds likely constitute a separate biogeographic population, with little contact with the main one discussed here (Fig. 1). Assuming the continuing effects of global warming, we can predict further separation of the two sub-populations and that Unit#4 (Fig. 4), the coast of Gotland and the islands and bays in Estonia will most likely play an increasingly important future role as winter quarters for this species. This is likely to be the case at other sites within eastern Baltic where this species can find suitable habitats.
Our historical analysis has shown that after a period of most rapid decline during 1988–2003, this population could be interpreted as remaining stable during 2003–2018 (Fig. 2). We suspect that this may be partly the result of the significant decrease in the Scaup bycatch in the Netherlands29,30,31. The added mortality from fisheries bycatch represents one of the most important threats to the relatively long-lived Scaup32. Evidence showed that drowning mortality was extremely high between 1985 and 1994, when an estimated average of 17,672 birds died annually in fishing gear (6% of the total population of the time), but this has declined since the 2000s32. Of all Scaup from this flyway population that drowned in fishing nets in years 1978–1990, up to 65% died at the most important wintering site at the time—the Dutch IJsselmeer32. However, our highly uneven knowledge of the extent of the Scaup bycatch throughout its winter range should be taken into account here. Exceptionally detailed estimates from IJsselmeer during the earlier period14 contrasts our lack of data or poor estimates from elsewhere, which may result in a bias that implies a greater importance for Scaup bycatch at the IJsselmeer for the population than was actually the case. Current estimates of bycatch levels throughout the flyway suggest that Scaup death in fishing nets has decreased, amounting to c.4000 individuals yearly, partly explained by the substantial decrease in the Dutch bycatch32.
The second highly important threat to Scaup, perhaps as important as the bycatch, is the deterioration of their food resources. Detailed energy budget studies on Lake IJsselmeer14 suggested that foraging Scaup there were operating on the margins of energetic profitability and the limited number of important wintering sites elsewhere suggest that alternative sites are really scarce, implying that food availability at core wintering sites could potentially affect winter survival.
The specialist habitat selection of the Scaup restricts it to a narrow range of habitats during the wintering period where it aggregates in large concentrations, a factor which causes the entire wintering population to concentrate in relatively few locations. Potentially, this makes them more vulnerable at the population level than most other, more dispersed diving duck species. During the January 2015 count, 91% of counted birds were present at 31 locations in five countries (Denmark, Germany, the Netherlands, Poland and Sweden). The four most important locations supported over two-thirds of the total wintering numbers: namely IJsselmeer in the Netherlands, Barther Bodden and Greifswalder Bodden in Germany and Odra river estuary in Poland (Fig. 4). Taken together, these areas have consistently been the most important wintering areas for Scaup over the last 30 years3,14,20, with two thirds of the flyway population during winter concentrated within 5300 km2 (2000 km2 in the Netherlands and 3300 km2 in Poland/Germany).
Wintering areas in Germany and Poland also act as stopover sites, so much larger numbers are counted there in autumn and spring migration, with up to 100,000 individuals on the Szczecin Lagoon (c.470 km29). Similarly, in Estonia, where a few hundred birds winter (Fig. 1), numbers may exceed 100,000 individuals in spring33. Therefore, cohesive planning for the effective conservation of the species, requires adequate protection at both the most important wintering sites (analysed in this article) and stopover sites along the entire migration route. During spring migration, extremely large Scaup concentrations can occur in these important sites, which provide for other biological functions such a communal courtship, displaying, pair-bonding etc.32. Given that Scaup are among the most vulnerable of diving ducks to bycatch34 (constituting more than 50% of diving birds drowned in fishing nets in the Polish Odra Estuary35) potentially high mortality during the prelude to the breeding season is likely to have severe adverse effects on the entire population. It is important to remember that this site can simultaneously support up to 75% of the total population9 and intensive fishing takes place here with gillnets35—the method of fishing recognised as the most dangerous for drowning diving birds in the Baltic Sea6.
Other environmental pressures on Scaup are no less serious, but currently less well quantified. Many important wintering areas are situated in estuaries of large rivers that invariably host major sea ports, where large vessels cause disturbance and pollution. Maintenance of shipping channels requires dredging (as in the case of the channel leading to the port of Amsterdam on IJsselmeer in the Netherlands and that serving the port of Szczecin on the Szczecin Lagoon in Poland). Dredging of shallow marine and brackish substrates can disrupt sediment horizons, mobilising suspended material, creating turbidity and disrupting the food resource and the ability of Scaup to forage for their prey. The proximity to human settlements also makes these shallow marine waters attractive to the increasing practice of water sports, kite- and wind-surfing, boating and recreational fishing from boats, which although not a source of direct mortality, contributes to disturbance and displacement of Scaup from favoured areas36,37.
SPAs and effectiveness of protection
The long-term conservation aim for a decreasing qualifying species, in accordance with European Union (EU) law (Birds Directive—Council Directive 2009/147/EC), should be to recover them to former level of abundance. To achieve this aim, SPAs should be designated in sites where 1% or more of the biogeographic population regularly occurs. In the case of Scaup, all of such areas are protected in the form of SPA (Table 2). Subsequently, such a SPAs should have a Management Plan (MP) defining the conservation objectives within each site, updated every 6 years. Of the three most important Scaup SPAs in Europe, only the IJsselmeer (NL9803028, Unit#2, Core wintering area, Fig. 4) has a MP for 2013–201738, which described the long term decline (since 1994) in wintering numbers of Scaup in the IJsselmeer and identified the greatest threats for Scaup as declining food resources and disturbance by developing water sports. Although bycatch was conspicuously not listed as a threat, the MP documents previous measures, taken to reduce fishing effort, had resulted from the implementation of another EU Directive—the Water Framework Directive (WFD, Directive 2000/60/EC). The WFD committed EU Member States to achieve good qualitative and quantitative status of all water bodies by 201538. Conservation measures carried out on Lake IJsselmeer over the last 75 years aimed to maintain sustainable fishing did not bring about the intended results on fish stock39. However, they may have had a positive effect on reducing bycatch of Scaup from 11,500 killed annually during 1978–199032 to insignificant numbers in the years 2011–201231, which may have contributed to the slowing in the rate of population decline at this time. In the most important wintering area for this flyway populations—the lagoons and bays either side of the German-Polish border, out of ten SPAs forming one coherent area (Fig. 4) only two have MPs. Moreover, the key SPAs within this area that regularly hold the highest Scaup numbers do not have MPs, they are: Greifswalder Bodden und südlicher Strelasund (DE1747402) in Germany and Szczecin Lagoon (PLB320009) in Poland. The Greifswalder Bodden, Teile des Strelasundes und Nordspitze Usedom (DE1747301) Special Area of Conservation (SAC), which overlaps with the Greifswalder Bodden und südlicher Strelasund SPA, was created under the Habitats Directive (Council Directive 92/43/EEC) and has a MP that identifies the threats to Scaup (e.g. from bycatch). However, because MPs for SACs (as against SPAs) are not primarily directed towards bird conservation, there are no specific regulations to limit the current stressors upon Scaup at this site40. The existing MPs for two other SPAs (“Vorpommersche Boddenlandschaft und nördlicher Strelasund” and “Dolina Dolnej Odry”) either do not identify main threats to Scaup or fail to impose sufficient conservation measures41,42.
Other SPAs that are less important for Scaup within Unit#3 west of the core wintering area include Östliche Kieler Bucht (DE1530491) and Ostsee östlich Wagrien (DE1633491), which have MPs identifying the threat from bycatch. This includes a voluntary agreement between the Schleswig Holstein Ministry of the Environment and local fishery associations, under which areas are closed to fishing if “concentrations of ducks” ( > 100) are present in the areas. Fishermen have two days to remove their gear after closure. There are rigid legal provisions at these two sites that prohibit fishing with gillnets within 200 m of the shore43,44. To date, there is no evidence of a positive effect and reduction of bycatch of diving birds, so we recommend a study of the effectiveness of these provisions.
The shift in the centre of gravity of the wintering population to Germany and Poland highlights the ineffectiveness of conservation measures directed towards Scaup (and other diving birds) there. Despite the existence of SPAs in which the Scaup is specifically protected and evidence of the cost of gillnet bycatch to local diving ducks, the most serious pressure remains unchecked. In 2011–2012, results from research work in the Szczecin Lagoon37 recommended the MP proposed reducing the Scaup (and other diving birds) bycatch by spatiotemporal regulation of gillnet fisheries to avoid key areas used by the birds. Unfortunately, the effective solutions to deliver results for bird conservation were considered too far-reaching by fishing interests. The fishing lobby blocked official approval of the MP by government and so these measures were never implemented. Given the high rates of Scaup bycatch, the designation of the area as a SPA offers no effective protection to the species at this site32. The effectiveness of SPA designation for a particular species remains ineffective, as long as effective management is not implemented. Given the increasing relative importance of the German/Polish resorts to the species in recent years, the lack of effective measures within these SPAs is becoming more critical to safeguard the conservation of the North-West Europe population of Scaup. Suitably prepared MPs, containing a bycatch monitoring order, would solve this problem, setting bycatch thresholds, according to the recommendations of BirdLife International45—1% of natural mortality calculated on the basis of local species abundance. If this value is exceeded, spatiotemporal restrictions on gillnet fishery should be introduced.
Looking to the future, areas that were formerly stopovers are already becoming wintering sites in Sweden and Estonia. Although currently not numerically significant in winter, these sites already hold significant numbers during migration. In the future, satellite areas (Unit#4) have the potential to develop into important wintering grounds and therefore require adequate protection from factors known to affect Scaup survival.
Previous studies show that bycatch in fishing nets is one of the most serious anthropogenic pressures during the non-breeding period for many diving birds6, although we cannot exclude the influence of other factors such as food availability and quality26 and disturbance from hunting46 and water sports37. Because the majority of North-West Europe’s Scaup winter in relatively few places, conservation interventions at these key sites are particularly important. The shift in wintering distribution poses new challenges for countries increasingly responsible for the conservation of this species in winter. Lack of adequate protection in this region means that these areas may act as sink habitats (in the sense of the source-sink model 47). The shift of wintering areas to sink habitats exposes an increasing part of the population to the pressures present there. This is not only the case for Scaup but also for a range of other diving bird species. These birds concentrate in the most attractive areas rich in food, often biologically productive transitional waters, where marine and freshwater birds meet in high densities. For this reason, effective conservation measures directed at Scaup will positively impact upon a whole range of other species with similar ecology. This suggests that protection measures taken for the Scaup could also benefit associated marine species in the same areas such as Long-tailed Duck, Velvet Scoter, Common Scoter Melanitta nigra, as well as for coastal zone species such as: Tufted Duck, Smew Mergellus albellus, and Goosander Mergus merganser. More

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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.
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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

Greenhouse experiments and sampling
Wheat (Triticum turgidum cv. Negev) was cultivated in sandy loam soil (19% clay, 6% silt, 75% sand) classified as Calcic Haploxerept. The soil was obtained from intensive agriculture field located in Eshkol region, Israel (31.248,949, 34.379,872). Potatoes, wheat and peanuts were previously grown in this field. Initial soil parameters were: pH 8.78 ± 0.04, electrical conductivity 99 ± 1 (µS/m), NO3-N 0.22 ± 0.02 (mg/kg), NH4 0.30 ± 0.01 (mg/kg), P-PO4 0.09 ± 0.01(mg/kg), total soluble organic carbon 4.0 ± 0.04 (mg/kg) and total soluble nitrogen 0.70 ± 0.02 (mg/kg).
The plants were grown for 6 weeks (from December 2016 to February 2017) as described previously [25]. Briefly, 750 g of soil was distributed in a 700-mL plastic pot, with four seeds per pot. Those pots were able to sustain up to four wheat plants for six weeks under the experimental conditions. The wheat was grown in a greenhouse with two closed-system chambers at day/night temperatures of 25 °C/18 °C ± 1 °C, and with an automatically adjusted CO2-supply system (Emproco Ltd., Ashkelon, Israel). The photoperiod was 9 h and the daily light integral was 12.5 MJ/day. Wheat plants were grown in a sequence of three independent experimental cycles of 6 weeks each (five pots per treatment per cycle), with a 1-week shift between cycles. Plants were grown under either ambient (400 ppm) or elevated (850 ppm) atmospheric CO2 levels. Nutrient solution was prepared with 90% nitrogen supplied as nitrate and 10% supplied as ammonium using KNO3 and NH4NO3 to provide final concentrations of 30, 70 and 100 ppm nitrate [26]. Other macronutrients were supplied in each treatment at the following rate: P-15 ppm, K-150 ppm, Mg-24 ppm, Ca-120 ppm and S-40 ppm provide by NH4NO3, KNO3, CaCl2, KCl, MgCl2 and KH2PO4 salts. 40 ppm S and Ca were present in the tap water. Micronutrients were supplied at a rate of 1.3 ppm Fe, 0.7 ppm Mn, 0.3 ppm Zn, 0.05 ppm Cu, and 0.0375 ppm Mo using Korotin (Haifa Chemicals, Israel), a commercial micronutrient mix. Each pot was irrigated with 50 mL of the nutrient solution four times a week. The total amount of nitrogen in the 30 ppm nitrate treatment was 36 mg/pot (equivalent of ca. 73 kg N/ha), 70 ppm nitrate treatment was 84 mg/pot (equivalent of ca. 170 kg N/ha) and in the 100 ppm treatment, 120 mg/pot (equivalent of ca. 250 kg N/ha).
Soil and plant analyses
At the end of the 6th week of growth, 15 pots (5 pots per cycle) from each treatment were sampled for soil, shoots and roots, and the following parameters were measured: soil nitrate and ammonia content, soil EC and soil pH, shoot and root dry biomass, nitrogen concentration and content in shoot and roots. Soil properties and relevant methods were as described previously [25]. Briefly, soil EC and pH were determined in a solution of 1:5 air dry sieved soil:distilled water (w/v). Nitrate and ammonium concentrations were determined using an autoanalyzer (Lachat Instruments, Milwaukee, WI or Gallery Plus, Thermo Fisher Scientific, Waltham, MA, USA). Sampled shoots and roots were dried at 60 °C for 48 h, ground and weighed to obtain dry biomass. Total nitrogen concentration was determined using an autoanalyzer (Lachat Instruments or Gallery Plus) following digestion with sulfuric acid and peroxide [27].
Root DNA extraction for sequencing and qPCR
At the end of the 6th week of wheat growth, pots were randomly selected for DNA extraction. To obtain the root-surface-associated microbiome, wheat roots were collected in triplicate from each of the three cycles and were vortexed three time with 85% saline solution, until no visible soil particles were attached to the roots. Total DNA was extracted from 0.4 g of complete root system, using the Exgene Soil DNA mini isolation kit (GeneAll, Seoul, Korea) according to the manufacturer’s instructions.
Generation of qPCR plasmid standards
Plasmids containing the 16S rRNA gene were generated as described previously [28, 29]. Each PCR amplification product was ligated into pGEM-T Easy Vector (Promega, Madison, WI, USA) and plasmids were transformed into BioSuper Escherichia coli DH5α competent cells (Bio-Lab, Jerusalem, Israel). Circular plasmid DNAs were used as the standards to create calibration curves at 10-fold dilutions for gene quantification by real-time qPCR.
Assessment of gene copy numbers by qPCR
Copy numbers of the total bacterial community (16S rRNA gene) and translation elongation factor 1 (TEF, a plant housekeeping gene) were assessed using selected primers (Table S1) in roots of 6-week-old wheat plants with the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Triplicates from whole genomic DNA were diluted to 6 ng/µL and 1 µL was used in a 20-µL final reaction volume together with 50 µM forward and reverse primers and 10 µL 1X FAST MasterMix (Thermo Fisher Scientific). Three biological and three technical replicates were analyzed for each root DNA sample. Reaction efficiency was monitored in each run by means of an internal standard curve (constructed plasmids) using duplicates of 10-fold dilutions of standards ranging from 108–102 copies per reaction. Efficiency was 89–98% for all target genes and runs, and R2 values were greater than 0.99. Copy numbers of the target genes were calculated based on the relative calibration curve of the plasmid copy numbers. All data analyses were conducted using StepOne software v2.3 (Applied Biosystems).
Shotgun sequencing
Root DNA was extracted from each of the biological triplicates, in each of the three cycles. For sequencing, the DNA of the triplicates was combined, resulting in three biological replicates per treatment (one from each batch) and 18 samples altogether. Shotgun metagenome libraries were prepared using the Celero DNA-Seq library preparation kit (NuGen, Takara Bio, USA) with enzymatic shearing, according to the manufacturer’s instructions. All libraries were then pooled in equal volumes and size selection (350–400 bp fragments) was performed using a Blue PippinPrep instrument (Sage Scientific). The libraries were then sequenced using an Illumina MiniSeq instrument employing a mid-output kit. Based on the number of reads per sample, the samples were repooled with varying volumes, and size selection was performed again using the same size range. The final size-selected pool was sequenced on an Illumina NovaSeq instrument with an S4 flow cell, employing 2 × 150 base reads. Library preparation and pooling were performed at the University of Illinois at Chicago Sequencing Core (UICSQC), and sequencing was performed by Novogene Corporation (Chula Vista, CA, USA).
In total, we obtained 310 Gb of information, with 30–44 million sequences per root sample. These sequence data were submitted to the Sequence Read Archive (SRA) of the NCBI databases under accession numbers SUB6631533 and SUB8385777, BioProject: PRJNA592741.
All reads were subjected to quality control using FastQC v0.11.3 [30] and barcode trimming using Trimmomatics v0.32 [31]. Reads were mapped to the whole wheat metagenome using Bowtie2 v2.3.5.1 [32], and mapped reads were filtered out from each sample. Then, short Illumina reads from triplicates of each nitrate treatment (30, 70 and 100 ppm) were assembled using SPADES v3.13.0 [33] into longer contigs, to create three wheat root microbiome catalogs for each treatment separately. The 30 ppm nitrate catalog had 677,271 contigs with N50 of 964 bp, 70 ppm nitrate catalog had 644,394 contigs with N50 of 971 bp, and the 100 ppm catalog had 677,271 contigs with N50 of 964 bp. Those three catalogs were combined and Prodigal v2.6.2 [34] was used for protein-coding gene prediction. To create a non-redundant set of genes, we used CD-HIT-EST software v4.8.1 [35] with a similarity threshold of 95%. Those genes were used as the root gene catalog, which included 35 million partial genes. This gene catalog was searched against the non-redundant NCBI protein database using DIAMOND sensitive algorithm v0.9.24.125 [36] to assign taxonomic and functional annotations. Results were then uploaded to MEGAN Ultimate edition software v6.15.2 [37]. The LCA (lowest common ancestor) algorithm was applied (parameters used with minimum bit-score of 70, minimum support of 5% and 30% top threshold) to compute the assignment of genes to specific taxa. For functional annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [38] was used. Following annotation, to generate taxonomic and functional count tables, each library was mapped to the gene catalog with Trinity mapping software v2.8.4 [39], with Bowtie2-modified parameters (–no-unal –gbar 99999999 -k 250 –dpad 0 –mp 1,1 –np 1 –score-min L,0,−0.9 -L 20 -i S,1,0.50).
Data analyses
Significance of interactions between CO2 and nitrate levels on soil and plant parameters was calculated using two-way ANOVA the least-squares method, in JMP 14 Pro software (SAS Institute Inc., Cary, NC, USA). Differences between soil and plant parameters as influenced by interactions between CO2 and nitrate levels was calculated using Student’s t test in JMP 14 Pro software and statistical significance was set at P  More