Ecology

Wheat cultivars and growing conditions
In this study, pot and field experiments with the shade-tolerant cultivar Henong825 and the shade-sensitive cultivar Kenong9204 were performed. These two winter wheat cultivars were identified with different degrees of shade tolerance by our previous study17. Both cultivars are released by Hebei Province, China, which are the most widely planted wheat cultivars in North China Plain. The parental combination of Henong825 and Kenong9204 is Linyuan95-3091/Shi4185, SA502/6021, respectively. Henong825 is characterized by strong lodging resistance. Kenong9204 is characterized by suitable for moderate water and fertilizer. Two field experiments were conducted during the 2016–2017 and 2017–2018 wheat-growing seasons in the Luancheng agro-ecosystem experimental station of the Chinese Academy of Sciences, Hebei Province (37° 53′ N and 114° 41′ E; elevation at 50 m). The climate characterizing of the study region is summer monsoon. The mean temperature, total precipitation, and solar radiation in both the winter wheat-growing seasons are shown in Table 5. The soil used in the experiments was loam containing 21.41 g kg−1 organic matter, 109.55 mg kg−1 alkaline nitrogen (N), 1.44 g kg−1 total N, 15.58 mg kg−1 available phosphorus (P), and 220 mg kg−1 rapidly available potassium (K). In both seasons, soils were fertilized with urea (N, 46%) and complete fertilizer (N–P, 21–54%) at 300 kg ha−1 and 375 kg ha−1. Seeds were sown by hand on October 6, 2016 and October 17, 2017, then the seedlings emerged 1 week later. In 2017 growing season, the YM stage was on April 15, and anthesis stage was on May 1 in both cultivars. In 2018 growing season, the YM stage was on April 16, and anthesis stage was on May 2 in both cultivars. The seedling density was 166 m−2, which is the norm in this region.
Table 5 The monthly mean temperature (°C), total precipitation (mm), and solar radiation (MJ m−2 day−1) during the two growing seasons of winter wheat in 2016–2017 and 2017–2018.
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Experimental design
This study was a combination of field experiment and pot experiment to investigate the effect of different shading intensity and duration during YM stage on grain components and photosynthetic characteristics. Pot experiment was supplement to field experiment.
Field experiments
The experiments were arranged in a randomized split-split plot design with three replicates. The main plots were split into three subplots subjected to one of three shading intensities: 100% (CK, control), 40% (S1), and 10% (S2) of natural light. Each subplot was split into four sub-subplots, which were randomly allocated to one of four durations: 1 day (D1), 3 days (D3), 5 days (D5), and 7 days (D7) during the YM stage. The shading treatments were conducted in these periods and replicated three times. Each plot size was 6 m long and 2 m wide, with 40 rows. There were 72 plots. Different degrees of artificial shade were provided by using black polyethylene screens horizontally installed at a height of 2 m above the ground.
Determination of YM stage
The YM stage roughly corresponds to Zadok’s scale from Z37 (main stem with flag leaf is visible) to Z39 (flag leaf ligule is noticeable). According to previous researches of YM stage, the estimated measurement of the YM stage was based on the auricle distance (AD, the distance between the auricle of the flag leaf and the auricle of the penultimate leaf) of main stem43,44. In order to keep the relationship between the occurrence of YM and AD unchanged, the field management practices, adequate irrigation was the same in two growing-seasons. Moreover, for each experiment, at the onset of appearance of the flag leaf of the main stem, 30 anthers of ten main stem spike of wheat were randomly sampled to establish the timing of YM stage initiation1. The correlation of the AD with the development of the YM stage in the florets of the two cultivars was measured and observed using microscope (Fig. 9). The cultivar Henong825 reached the YM stage at 1–2 cm, whereas Kenong9204 reached the YM stage at − 1 to 0 cm. To capture the YM stage in the shading condition, the plants were subjected to shading stress ahead of the YM stage occurrence. When more than 50% of the plants in each plot reached − 2 cm in Henong825 and − 4 cm in Kenong9204, the main stem of the plants was tagged, and shading stress was applied in each plot. Each experimental plot for Henong825 and Kenong9204 was independently subjected to shading stress on April 15, 2017 and April 16, 2018. When the shading stress treatments ended, the shade screens were removed and were exposed to natural light until they matured. Air temperature, light intensity, and relative humidity above the canopy were recorded using a portable weather station (ECA-YW0501; Beijing, China) during the shading period. Light spectral was measured using a portable geographic spectrometer (PSR + 3500, USA). The irradiance of spectral wavelength ranging from 350 to 2,500 nm was measured. The proportions of blue light (B/T), green light (G/T), red light (R/T), far-red light (FR/T), and red/far red (R/FR) were calculated according to their irradiance at 400–500 nm, 500–600 nm, 600–700 nm, and 700–800 nm, respectively. Following the local field management practices, adequate irrigation was conducted three times during the overwinter, jointing, and anthesis stages of the wheat-growing season. Weeds, fungal diseases, and insect pests were controlled through spraying of conventional herbicides, fungicides, and insecticides, correspondingly.
Figure 9

The relationship between anther development and shading period in two wheat cultivars.

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Pot experiments
The pot experiments were conducted in a temperature-controlled glasshouse. Vernalized seedlings of the two wheat cultivars were transplanted to pots (45 cm in length, 28.5 cm in width and 20 cm in height; 18 plants in each pot; three pots for each treatment group) containing a mixture of vermiculite and nutritional soil (1:1). All wheat seedlings were grown at a day temperature of 25 °C, night temperature of 15 °C, and light intensity of 800 μmol m−2 s−1. When the AD of the main stems of Henong825 and Kenong9204 cultivars were approximately − 2 cm and − 4 cm, respectively, the main stem of the plants was tagged, and shading stress was applied in each treatment. Shading treatments groups were the different shading intensities and shading durations previously mentioned. The shading condition in glasshouse was simulated with black polyethylene screen to keep up with the experimental methods in the field. After shading stress, the shading nets were removed, until the crops matured.
Sampling and measurements
Photosynthetic rate, stomatal conductance, intercellular carbon dioxide, and chlorophyll fluorescence parameters
In field experiments, three randomly selected flag leaves on the tagged main stems of plants in each plot were analyzed to determine Pn, Gs, Ci, and chlorophyll fluorescence. For each shading treatment group, Pn, Gs, and Ci were measured using an LI-6400XT portable system (LI-COR Biosciences, Nebraska, USA), and the chamber of which was equipped with a red/blue LED light source (LI6400-02B) before the shading stress was removed. Before measurement, the machine was preheated for 30 min, and checked, adjusted to zero, calibrated according to the instructions. Moreover, the light intensity in measured chamber was equivalent of shading treatment conditions. The flow rates was set at 500 μmol s−1, The temperature in chamber was set 25 °C. The CO2 concentration was set to 400 μmol mol−1, which was provided by carbon dioxide cylinders to maintain a stable CO2 environment. The chlorophyll fluorescence of flag leaves on the tagged main stems of plants were measured using a modulate chlorophyll fluorescence imaging system (Imaging-PAM; Hansatech, UK) in each plot. The primary light energy conversion efficiency of PSII (Fv/Fm) and actual photochemical quantum efficiency (YII) were measured after 30 min of dark adaptation. The saturation irradiance (PARsat) and maximum electron transport (Jmax) of flag leaves in each treatment were calculated using a modified rectangular hyperbola. On the day next to shading removal, the Pn of three flag leaves from each replicate plot were measured.
Chlorophyll content
For glasshouse pot experiments, nine flag leaves (three leaves were randomly selected per pot from three pots in each treatment group) tagged main stems of plant were selected prior to the removal of shading. The flag leaves were then sliced following the removal of the main vein. After the sliced fresh leaves were weighed to 0.1 g, the chlorophyll content of leaves was extracted with 80% acetone for 48 h and analyzed through micro-determination (Thermo Varioskan Flash, USA). The absorbance of chlorophyll a (chl a) and chlorophyll b (chl b) was read at 663 and 646 nm, respectively (Thermo Varioskan Flash, USA), and the chlorophyll contents were calculated according to following equations: chl a (mg/g) = (12.7 × A663 nm–2.69 × A646 nm)/(100 × M); and chl b (mg/g) = (22.9 × A646 nm–4.68 × A663 nm)/(100 × M) where A663 and A646 are absorption levels at 663 and 646 nm, respectively; M is leaf fresh weight. The total chlorophyll (chl a + chl b) values were calculated by chl a and chl b values.
Leaf anatomy and surface characteristics
The approximately 2-mm2 leaf sections in D7 treatments and one day after recovery were harvested from the center of three flag leaves on the tagged main stems of plants using a scalpel and were rapidly fixed in electron microscope fixation fluid at 4 °C overnight. Stomatal apertures and chloroplast ultrastructure were observed by Servicebio (Wuhan) using a scanning electron microscope (SU8100; Hitachi) and a transmission electron microscope (HT7700; Hitachi). Simultaneously, the fully expanded flag leaves collected from plants in each treatment were fixed with FAA solution and embedded in paraffin to measure the leaf anatomical structure. The embedded wax block were sectioned to a thickness of 8 μm, then following dewaxing in environmental transparent solution and rehydration in a series of graded alcohol solutions. Finally, the tissue samples were stained with safranin and fast green, observed under a Leica DM6 microscope (Leica, Germany), and the respective images were obtained.
Grain yield, yield components, and aboveground biomass
At harvest in the field experiments during both growing seasons, 60 tagged plants per replicate were randomly sampled to determine grain yield components. The harvested plants were naturally dried to a grain water content of approximately 11%. Each tagged plant was then threshed using a single plant threshing machine to determine the grain number and grain yield needed for the estimation of the average grain weight. In addition, 30 tagged winter wheat plants were uprooted randomly and gradually by hand from each plot. Each plant was cut from the root and was dried at 80 °C. Aboveground biomass was measured using a precision digital balance (model BSA3202S; Sartorius, Germany) with a precision of 0.01 g.
Statistical analysis
The experimental data for grain yield, yield components, biomass and chlorophyll fluorescence parameters were analyzed using a general linear model procedure (GLM) in SPSS 22.0 for a split-split plot design. The significant differences among treatment mean values were determined by the least significance difference analysis (LSD, P  More

IR1 invades colonies of other bacteria on low-nutrient agar plates
A screening was made for bacteria that interacted when in close proximity with IR1 colonies on ASWBLow agar plates. The source of the bacteria was the same as IR1: sediment and the brown alga Fucus vesiculosus from brackish water near Rotterdam Harbour (NL), after storage of original samples at −80 °C for 5 years (Tables S1 and S2 for strains used). The most common form of interaction found was that motile, gliding cells from colonies of IR1 overgrew and degraded some adjacent colonies. The bacteria that were vulnerable to IR1 were identified on the basis of 16S rRNA sequencing and found to be Moraxella osloensis, Staphylococcus pasteuri, Pseudomonas spp., Pedobacter spp. and Enterobacteria cloacae. The latter were repeatedly isolated and strain B12 was chosen for further work. In contrast, successful competition by IR1 over B12 was not seen in liquid culture or a submerged biofilm model (Supplementary Fig. S1). SC was also not observed in liquid culture nor biofilms.
Competition between IR1 and B12 co-inoculated on an agar surface
Further competition experiments were performed between IR1 and GFP-expressing B12(pGFP) on low-nutrient agar plates to determine the basis of the competitiveness of IR1. The two strains were co-inoculated as a 10 µl spot on ASWBLow plates, which were then incubated at 22 °C for up to 2 days. Within the area of inoculation, IR1 reduced the numbers of viable B12 to below the initial inoculation level, suggesting an active killing mechanism. Replacing the cells of B12 with similar numbers of fluorescein-labelled latex spheres (0.2–2 µm diameter) resulted in no significant redistribution of the spheres by growing IR1. This suggests that IR1 was not simply pushing bacterial-sized objects outside the imaging area. Outside the area of (co-)inoculation, the more motile IR1 dominated completely (Fig. S2b, c) and was able to disengage from B12 and form axenic gliding groups. Imaging of B12(pGFP) and IR1 indicated that B12 was not present within emerging masses of IR1 (Fig. S2b, c).
IR1 grows on living cells of B12 on starvation medium suggesting predation
The interaction between IR1 and B12 was tested on agar plates that contained insufficient nutrients for the growth of either strain alone (starvation medium). IR1 was inoculated directly on a starvation plate previously spread with either dead or alive B12 (that had been repeatedly washed to avoid carry-over of nutrients). Both dead and living B12 cells supported progressive colony expansion (up to 0.5 mm day−1) by IR1 over a period of 12 days, compared to starvation medium alone (Fig. 1 and Fig. S2d). Live strain B12, in the absence of IR1, did not show a high level of propidium iodide (PI) staining on starvation medium and ASWLow suggesting that autolysis was not occurring (Fig. S3a, b). Therefore, IR1 appeared to be growing at the expense of B12; that scavenging (use of dead cells as nutrients) and predation (use of live cells of another species as nutrients) both occurred.
Fig. 1: Colony expansion of IR1 on starvation medium in the presence or absence of B12.

IR1 colony expansion rates (average of n = 3) were calculated over two weeks. Shaded circles, IR1 on agar without B12. Solid circles, IR1 on agar covered with living B12. Open circles, IR1 on agar with dead B12.

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Invasion of B12 by IR1 is first by infiltration and then by undercutting of B12
In order to visualize the early stages in predation, an assay was created where spots of IR1 and B12(pGFP) were inoculated 3 mm apart on ASWBLow agar. This “encounter” assay allowed growth of both strains, motility of IR1 but not B12, and monitoring by microscopy of the early interactions upon contact. Initially, IR1 expanded equally in all directions, showing no directed movement towards the B12 colony. Contact between two colonies (on the mm scale) was therefore driven by gliding IR1 and was accidental, not directed. After contact, the following stages in predation were observed:
Stage 1 (1–4 h after contact)
Cells of IR1 infiltrated the B12 colony. The IR1 cells were flexible (Movie S1) and moved through dense masses of B12. In addition, IR1 cells moved around the periphery of the B12 colony to surround it, as detectable by the SC displayed by IR1 (Fig. 2a).
Fig. 2: IR1 invades and predates adjacent colonies of B12.

a Inoculation of IR1 adjacent to B12(pGFP) on ASWBFLow plates (ASWBLow agar supplemented with 0.5% w/v fucoidan), showing the result 10 h after contact between the spreading colony of IR1 and the static mass of B12. IR1 surrounds the B12 colony (w) and creates breaches (x) in the thicker edge of the B12 colony and a shift from dull purple/red SC typical of growth on ASWBFLow to green (y). IR1, IR1 colony; B12, B12 colony. b–d Images 4 h after contact with invading IR1. b Illumination from side showing white B12, with a thicker colony at the periphery (z) and SC from IR1 (bright pinpoints of colour including deep within the B12 colony) (y). c Fluorescence image showing GFP expressed by B12. d Merged (b) and (c). e–g are similar to b–d but after 9 h showing more extensive clearing of B12 cells and major breaches at periphery of the B12 colony (x). h and i show an experiment where B12 is inoculated in a droplet on to starvation medium, allowed to dry and then IR1 inoculated inside B12. h Result after 4 days showing expansion of the IR1 colony (IR1, showing predominantly green SC) to breach the periphery of the B12 colony (opaque white) from within. i Result of the same colony as (h) after 8 days showing progressive destruction of the B12 colony and movement around the periphery of B12 to engulf it. Scale bar indicates 0.4 mm for (a), 0.15 mm for (b–g) and 0.5 mm for (h) and (i).

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Stage 2 (4–20 h after contact)
Channels were created through the periphery of the B12 colony by groups of IR1 (Fig. 2a–d).
Stage 3 (after 20 h)
Penetration of IR1 cells into the B12 colony interior occurred through increasingly large breaches at the periphery of the prey colony, spreading to hollow it out. In this stage, groups of hundreds to thousands of cells of IR1 moved into B12, in an arrangement reminiscent of roots pushing through soil (Figs. 2e–g, 3 and Movie S2). Initial progress through the B12 colony was rapid, up to 60% of the rate at which IR1 spread over agar in the absence of B12, i.e., up to 5 mm h−1.
Fig. 3: Invasion of B12 by IR1 imaged by confocal microscopy.

a–c Three images taken from a Z-slice of a colony of B12(pGFP) during predation by IR1 (unstained, lines of advance shown with white arrows). From left to right the three slices show B12 cells at the agar surface, then 5, and 10 µm heights. d Overview image assembled from multiple contiguous images showing IR1 penetrating a colony of B12(pGFP). IR1 (not stained, visible as dark root-like regions but with an overall invasion route of top right to bottom left) is moving into a colony of GFP-expressing B12. White arrows show the direction of movement of some of the IR1 masses. Propidium iodide (red) is staining damaged cells (predominantly B12) within 20 μm of the major lines of advance of IR1. The scale bar in (d) indicates 50 µm when applied to (a–c) and 80 µm when applied to (d).

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Because of the intense SC displayed, shifts in the organization of IR1 cells could be inferred from alterations in colour visible during invasion of the B12 colony. When the agar medium contained high levels of fucoidan, the predominant colour displayed by IR1 was a dull red purple/red (Fig. 2a, b). However, SC was more noticeable when IR1 contacted B12 and particularly an intense green colour within the B12 colony. This suggested a high degree of local organization, as a 2DPC [1], when IR1 was interacting with B12. It was notable that both the steps in predation described above, and formation of the 2DPC, were unaffected by illumination (using a broad-spectrum white LED which was optimal for viewing SC) over a 48 h period.
Inoculation of IR1 inside a larger spot of B12 on starvation medium resulted in the growth of both strains (particularly IR1); IR1 both formed a uniform SC and degraded the B12 until it reached the edge of the colony (Fig. 2h, i). At this point, IR1 then rapidly moved around the periphery of the B12 colony in less than a day, effectively engulfing it (Fig. 2h, i).
Confocal microscopy of B12(pGFP) at leading edges of IR1 during stages 2 and 3, at different depths, indicated that groups of cells of the invading IR1 were able to undercut B12 (Fig. 3a–c); i.e., the front edge of IR1 made the greatest progress into dense masses of B12 at the agar surface. IR1 interposed a dense mass of cells between the nutrient-containing surface and the mass of B12 cells above. However, after that point (50 µm behind the leading edge) IR1 cells extended from bottom to top of the colony, i.e., over 20 µm in height. This was the case for a high-density colony (inoculation of at least 5 × 108 cells cm−2) of B12.
The killing of B12 by IR1 is short range and inhibited by excess nutrients
On rich medium, i.e., ASWBC or ASWB agar (both containing 5 g l−1 peptone, the former containing 5 g l−1 κ-carageenan in addition to the other components of ASWBLow agar), IR1 was motile but failed to predate B12 during the first 4 days of contact. On ASWBLow plates, during invasion of a B12 colony confocal microscopy of B12(pGFP) cells immediately adjacent to the invading IR1 did not reveal any change in morphology of B12 (Movie S2 and Fig. S3). In order to investigate the action of IR1 on B12, predation assays were created in which B12(pGFP) and IR1 were inoculated adjacently as before, but PI was used to stain damaged cells [27]. Imaging by confocal microscopy suggested that the cells of B12(pGFP) were absent from the main invading groups of IR1. The cells of B12 in close proximity to the leading masses of IR1 (108 cfu of IR1 cells were spotted within 5 mm, in which case motility appeared directed towards IR1 (Fig. 6). This suggests a degree of sensing and targeting of IR1; unlike the interaction of IR1 and B12, in which the initial collision between the strains appeared accidental, with the only specific interactions occurring after this event. Using a co-inoculation assay the ability of PIR4 to predate WT and mutant strains of IR1 (Fig. 6) was quantified. No significant differences were found, suggesting that motility and formation of a 2DPC did not provide resistance.
Fig. 6: Predation of IR1 by Rhodococcus spp. PIR4.

a Images of PIR4 (P, white) apparently moving towards and degrading a colony of IR1 (IR1 SC green) after 30 and 48 h (left and right, respectively). Scale bar indicates 5 mm. b Quantification of predation of GFP-expressing strains (WT and mutants) of IR1 by PIR4. C indicates a control (WT without PIR4). Replicates were threefold in arbitrary units of fluorescence; error bars indicate SD from the mean.

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As we hypothesized, the applicability of using SPM as source for fish eDNA metabarcoding has been confirmed and used for first time in this study. Fish species were found in all samples, irrespective of the location or characteristics of the SPM sampled.
Comparing the different extraction methods used, the eDNA extracted from SPM samples using a modified protocol of the DNeasy PowerSoil Kit, presented the highest purity (260/280 nm ratio) in combination with high DNA concentration, therefore it was the method selected for metabarcoding the eDNA extracted from the nine sampling sites. The isolation method was chosen due to its simplicity and scalability to perform a high number of extractions. However all tested methods resulted in high DNA concentration, making them suitable for metabarcoding, even if post extraction cleanup would have been needed (e.g. the Magnetic Forensic kit showed lower purity 1.44 (260/280 nm ratio)).
While eDNA-based fish monitoring from filtered water samples has been widely used and described and has cheap setup costs, it provides only a snapshot of the diversity at the sampling point, while continuous integration and eDNA settling in time-integrative sampled SPM would provide a better reflection of long-term site occupancy15,16,17,18. On the other hand, eDNA extraction from water samples using filters are laborious and extractions yields are low. The process of particles sinking or binding of eDNA (or residues of, e.g. fish tissue, feces or shales containing eDNA) to organic or mineral particles in SPM18 may result in a progressive accumulation of eDNA in the SPM. This statement was confirmed in our study. The results showed that using one SPM sample yielded higher DNA amounts per extraction (400–2,500 ng) than what is reported for eDNA extracted from an individual water sample using filters (30–560 ng)18,21,22,23,24,25. Here a small amount of SPM (~ 250 mg) is sufficient to extract high amounts of eDNA, which is of particular importance for the detection of rare fish species, where the concentration of their DNA is expected to be low. For example, Salmo salar which is classified as endangered in German rivers26, was detected in the Koblenz, Weil, and Blankenese SPM samples. Another main advantage of using SPM (in particular archived in the ESB), is that it is possible to retrieve and reanalyze the source material, allowing repeats and other complementary analyses e.g. chemical analysis to determine the presence of contaminants or stressors responsible for changes in fish populations. This kind of repeat analysis are not possible with filtered water samples, unless multiple samples are taken in parallel or the water itself is retained, both costly options.
Here, eDNA metabarcoding of the 9 riverine sites detected a total of 29 fish species. Most taxa found belong to commonly detected species in large rivers in Germany. For example, Abramis brama, Rutilus rutilus, Barbus barbus, Squalius cephalus, and Perca fluviatilis and are largely overlapping with the regulatory monitoring data from the Water Framework Directive (WFD)27. This coherence of fish species identified from eDNA extracted from SPM with the commonly detected fish species demonstrated the suitability of this approach. However, the number of fish species found in the ESB samples is similar or lower to what was found using traditional fish monitoring techniques, e.g. electro- and netfishing under the WFD27. For example ,with regard to monitoring sites in Germany between 27 and 57 fish species have been detected in 2012 and 2013 along the Rhine28, between 19 and 24 fish taxa were counted in 2007 at four sites of the river Elbe and between 27 and 29 fish species were detected at three sites of the Danube29. However, it needs to be considered that the number of WFD surveillance monitoring sites is much higher than the ESB sampling sites investigated in this study.
The fish community analysis also evidenced the presence of two contaminant species: Danio rerio and Oryzias latipes. For this reason, the extractions from the 9 sampling sites were repeated retrieving new subsamples from SPM, and before sequencing the absence of contaminant species (e.g. Danio rerio) was validated using specific qPCR primers (See Supplementary information). The specie-specific qPCR and the metabarcoding results showed successful removal of exogenous lab- contaminant fish species (See Supplementary information). The detection of those reads in the first samples strongly suggests cross-contamination in the laboratory since Danio rerio is a specie that we used commonly in our facilities for other purposes. It is well known that the most serious pitfall of metabarcoding eDNA is the risk of contamination with exogenous DNA30,31.
At the stage of PCR during library preparation, several samples exhibited unspecific amplification (double banding), Prossen, Weil, Bimmen and Dessau, which might be indicative of bacterial amplification. This additional bacterial amplification might have resulted in less efficient fish-specific sequencing and in consequence, a lower number of species found in those samples (5–9 species found compared to 8–17 species found in the non-contaminated samples). However, the richness is not only attributable to the presence or absence of contamination but might be also inherent to the sample. Contamination of reagents with bacterial DNA, or contamination with exogenous DNA in the laboratory (e.g. Danio rerio), in combination with the bacteria inherent to the sample itself, is a major problem exacerbated by the highly sensitive nature of the PCR, in particular when using universal primers. Therefore, even minor presence of these species in the lab equipment (like pipettes, surfaces, etc.) might result in large non-target amplification. To avoid such risk, we performed decontamination procedures for laboratory spaces and equipment (with UV radiation) and physically separated pre- and post-PCR workspaces.
The results of this proof-of-concept study will open the door for the retrospective evaluation of SPM samples to study, for example, seasonal and temporal trends of invasive species. The present study can be regarded as a first step towards more comprehensive investigations using eDNA extracted from archived SPM of freshwater fauna, flora and microorganisms. The fish taxa detected in this study complement well with species sampled in fish monitoring with traditional methods, e.g. nets, fykes and electrofishing. However, to study the fish community of a particular sampling site and draw conclusions on differences among sites, further investigations and more stringent analyses are required. The definition of a methodology should include an eDNA extraction strategy considering, for example, SPM extraction volume, the number of replicate extractions, the number of independent sequencing analyses required vs pooling the extracted DNA, etc. In order to validate this proof-of-concept study, future work will focus on method optimization and comparisons with established monitoring approaches. More

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Model
An ideal experimental set-up to study the effect of LCRs on bid prices would include (at least) two identical countries, completely independent of each other, with an auction scheme identical apart from the LCR feature. Any price difference that emerged between the two auction schemes could then be fully attributed to the differences in the LCR feature. Even better would be to include additional identical countries with varying levels of stringency in the level of the LCRs (in weight, value or number of components), to study whether there are discontinuities in bid prices that seem to be attributable to increasing levels of LCR stringency. For instance, one might expect a non-linear increase in bid prices if very high levels of LCRs (say, 95%) were introduced given that the manufacturing capabilities for wafers would be near zero in India35.
The Indian case comes close to an ideal policy experiment given that many tendered capacities were distributed between LCR and non-LCR auctions in similar geographic areas—albeit not always equally in terms of capacity. Importantly, firms were free to bid in both LCR and open auctions, and many firms submitted bids in both auction windows. However, to address the possible remaining issue of selection bias (that is, firms self-selecting into auctions with or without LCRs), we divide firms into two groups: firms that only bid in auctions without LCRs (59 firms, 134 bids) and firms that bid in both auction types, open and closed (26 firms, 143 bids). The data used in the study come from various sources from the Indian government and firm-level data from Mergent Intellect (described in more detail in the Data section).
We considered using a multinomial selection model that would divide firms into three groups: those that only bid in auctions with LCRs, those that only bid in auctions without LCRs and those that bid in both auction types. However, since there are only seven firms in the category of ‘only LCR auctions’, we were unable to run the model. Yet we believe that the main question is whether a firm bid in an LCR auction, because it indicates whether the firm has sufficient local knowledge to either liaise with a local manufacturer or to use its own existing manufacturing facilities. Firms that only bid in the open auction, in contrast, could merely import the required parts. Hence, we expect there to be systematic differences between these two groups.
Thus, we test whether firms that do not bid in the LCR category are different from those that do bid in the LCR category. It could be, for instance, that firms that bid in LCR auctions have more experience in local development than firms that only bid in the open auctions (where there are no restrictions in using imported material). Similarly, firms that only bid in the open auctions might be able to more effectively exploit economies of scale by producing solar PV cells and modules for several markets (for example, Canadian Solar, which has manufacturing capabilities in China).
In addition to using standard ordinary least squares regressions, we therefore make use of a Heckman regression model, which accounts for this possible selection bias. Heckman’s 1979 seminal paper proposes a two-step statistical approach37. In the first step, an economic model is defined in which plausible factors for the probability of falling into (in our case) either Group 1 or 2 are considered. This is modelled as a probit regression,

$${mathrm{Pr}}(G = 1|Z) = Phi (Zb)$$
(1)

where G indicates whether the firm belongs to Group 1 (G = 0 otherwise), Z is a vector of explanatory variables, b is a vector of unknown parameters and Φ is the cumulative distribution function of the standard normal distribution. The explanatory variables we consider are the number of employees of a given firm, whether it is a state-owned enterprise (SOE) and whether the firm is itself a manufacturer or is merely a project developer (an indication of the degree of vertical integration). We also consider whether the firm is primarily focused on energy or merely attempts to diversify from an unrelated field, indicating limited technical experience, and whether the company already bid in the NSM Phase I. The latter factor captures advantages that firms might have in the NSM Phase II due to prior experience with the auction system.
The second stage of the Heckman model then uses the probability that a firm will self-select into Group 1, based on its characteristics, by including that probability as an explanatory variable in the ordinary least squares regression.
For our standard ordinary least squares and Heckman regression model, we also created a number of explanatory variables that we assume influence bid price. We recognize that competition differed substantially between rounds and was on average twice as high in open auctions as in LCR auctions (as measured by our variable defined in equation (2)). Firms are likely to anticipate, or at least have beliefs about, the level of competition in an upcoming auction round, which leads them to adapt their bids accordingly (that is, to make higher bids when less competition is expected; this is well documented in the literature38). In order to exclude the possibility that bids under LCR regulation are higher solely due to this effect, we control for the degree of competition within each round. Therefore, we define the competition for each tender as follows:

$${mathrm{Competition}}_r = frac{{mathop {sum }nolimits_{n = 1}^N B_r}}{{AC_r}}$$
(2)

where B is the capacity in MW of each of the bids submitted for a particular auction round r, AC is the total capacity in MW auctioned in round r and N is the overall number of bids received for each auction round r. For instance, if 20 MW are auctioned off and firms submit 100 MW in bids, the competition would be 5.
We also include the cumulative installed capacity of each developer within the auction windows we cover to account for learning-by-doing of the developers and capacity building (for example, through greater local knowledge and connection to suppliers)39,40. Our time dummy controls for exogenous technological change, such as decrease in the cost of solar PV modules and other equipment over time, that is not directly related to the deployment in India (that is, exogenous technical progress)41. We do not include a state dummy as the variable is correlated too strongly with our time dummy (as certain states only conducted auctions in specific years, leading to high multicollinearity). We do, however, include the mean solar irradiation (annual average kWh m−2 d−1) per state to control for differences in the solar resources across different states (we also use the maximum solar irradiation for each state as a robustness check, which does not affect the results42).
In addition, we include a dummy for the utility that purchases the electricity generated by the awarded projects. It is well documented that the financial solvency of the utility buying the electricity (that is, the offtaker) is an important factor in assessing the risk associated with a project (that is, if an offtaker is less financially stable, the risk of a default increases, making capital more expensive, which in turn increases the cost of power43). Lastly, we consider whether a PV project being in a solar park has an effect on bid price. Solar parks are designated areas where environmental impact assessment, land procurement and interconnection are already taken care of. However, these increased costs may be reflected in the land price for the solar projects. By differentiating between solar parks and normal land, we are able to capture the price differences between the two approaches.
Thus, we use the following specification to study the effect of LCRs on bid price:

$$begin{array}{ll}{mathrm{bid}}_i & = alpha + beta _1{mathrm{LCR}}_r + beta _2{mathrm{Competition}}_r + beta _3{mathrm{Year}} + beta _4{mathrm{Cum}}_{{mathrm{MW}}} \ & + beta _5{mathrm{Offtaker}} + beta _6{mathrm{Solar}},{mathrm{park}} + beta _7{mathrm{Sol}} + varepsilon _iend{array}$$
(3)

where bidi is the individual bid of each firm, r is the auction round, LCRr is the dummy for whether local content was required or not in the auction, Year is the time dummy to control for temporal shocks, CumMW is the cumulative installed capacity prior to the given auction in the NSM Phase II, Offtaker is a dummy for the utility buying the power (1 = SECI, 0 = NTPC), Solar park indicates whether the project is within a solar park, Sol refers to the annualized average solar resources (kWh m−2 d−1) in each state and εi is the error term. We also include an interaction term between LCR and our time dummy, to test whether the effect of LCRs changed over time.
Part of the auctioned capacity was tendered under the viability gap funding (VGF) scheme, where the government fixed a base power purchase agreement (PPA) price and companies could request a top-up on the existing base price to make their project financially viable. Since price-only auctions have been implemented in India, the bidders who quoted the lowest amount of VGF were awarded the contracts until the auctioned capacity was reached (it should be noted that bidders were allowed to quote a lower PPA tariff than proposed and waive the VGF, but this rarely happened). Given that the VGF is dispensed as a capacity-based payment at the beginning of the lifetime of a power plant instead of as a constant subsidy for each unit of electricity generated, we had to levelize the amount to compare the outcomes with the generic auction results, where the payments are made across the entire lifetime of the power plant. Therefore, we applied the following method, which is based on the commonly used levelized cost of electricity (LCOE) calculation44, to calculate levelized VGF:

$${mathrm{VGF}}_{{mathrm{levelized}}} = frac{{{mathrm{VGF}}_{{mathrm{total}}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{E_t}}{{(1 + d)^t}}}} = frac{{C{mathrm{VGF}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{C{mathrm{Flh}}}}{{(1 + d)^t}}}} = frac{{mathop {sum }nolimits_{t = 0}^5 frac{{{mathrm{VGF}}_t}}{{(1 + d)^t}}}}{{{mathrm{Flh}}mathop {sum }nolimits_{t = 1}^{25} frac{1}{{(1 + d)^t}}}}$$
(4)

In equation (4), Et is the electricity generated in year t, C is the project’s capacity in MW and Flh is its full-load hours. We assume constant, region-specific full-load hours, which can be found in ref. 45. For bids that did not indicate the project’s location in India, we assume a capacity factor of 20% and thus full-load hours of Flh = 1,752 h. Moreover, we assume a discount rate of d = 10% and a plant life of t = 25 years. With our approach, we are also able to capture the time value of money induced by the different VGF disbursement methods applied throughout Phase II (Supplementary Table 7; note that there was no VGF disbursement in Batch II). We then add the resulting levelized VGF support to the specific PPA price.
To estimate the possible range of values for the additional cost borne directly by the Indian government due to LCRs, we used the average estimates from our Heckman regression of the additional cost of power of LCR bids when compared to the open bids. We compute this overall cost to the Indian government via an NPV model in which we discount all future payments from the Indian government to solar power plant owners and compare the cost to the clean technology budget in India. We use discount rates of 10%, 12% and 14% and a capacity factor of 20% for the solar PV plants and a 25-year running time based on REN21 (2018) data. These numbers are roughly similar (apart from possibly lower discount rates in this study) for other developing and emerging economies. These discount rates are based on information used by the Indian government for evaluating public projects46. Given the well-known challenges of choosing social discount rates (SDRs)47, we perform a sensitivity analysis by varying the discount rate between 10%, 12% and 14%. Taken together, these values for the SDRs encompass typical values of SDRs used in other developing and emerging economies, something that helps make our results more comparable to other countries46. We use the average real bid price from all open category auctions as our base price to calculate the additional cost of LCRs over the lifetime of an average solar project subject to LCRs.
In order to analyse the possible benefits of the LCR policy, we select a small set of indicators commonly used in the innovation systems and catching-up literature to determine whether a country is ‘narrowing’ the gap between the innovation leader and itself. While there are no perfect sets of metrics, we employ three different metrics commonly used in the innovation and economics literature: (1) domestic and international patent filings in the technology of interest40, (2) domestic production versus international imports5 and (3) exports to other countries from the country of interest4.
This analysis should be understood as correlational rather than causal, in contrast to the first part of our analysis. In addition, given how relatively recent the policy is, this analysis captures only short-term manufacturing and innovation effects. This is a limitation because some of the impacts of the policy, such as ongoing consolidation of the local industry through mergers and acquisitions, may take more time to materialize. Hence, the main contribution of this paper is the empirical assessment of the additional costs of LCRs, while the analysis of the possible benefits provides indicatory evidence of the evolution of important manufacturing and innovation metrics.
Data
In our analysis, we focus on the NSM Phase II auction results from 2014 to 2017. We did not include the bids and results from NSM Phase I since the majority of the projects (61% of total capacity deployed48) in the auction relied on thin film technology (as opposed to silicon panels), which was exempt from LCRs. Furthermore, we focused on the results of the PPA-based scheme and did not consider the EPC programme, which has a different focus: the auctioneer procures and owns the project and does not remunerate the electricity generated over 25 years to the project developer. The different remuneration mechanism, limited availability of data and different auction design elements, as well as different offtakers, hinder the comparability of the data. For the same reason, we neglect auctions conducted by state governments and focus solely on central government tenders conducted by either SECI or NTPC.
Contrary to most other countries conducting auctions, the Indian government shows a high degree of transparency in terms of publishing bids in the NSM Phase II auctions—including information on firms and the bid prices of both successful and unsuccessful applicants. We collected the data about the bid prices and the respective bidders from various government sources, either directly through governmental bodies (for example, SECI) or indirectly through different industry and reputable news sites, such as Mercom India or EQ International Magazine. We include all auction rounds of Phase II that had LCR regulations in place for a total of 28 auction windows across 10 Indian states. As shown in Supplementary Fig. 5, we intentionally excluded from the analysis the state-wise utility-scale PV tenders (around 14 GW by September 2017). In addition, we exclude the central government EPC tenders (1.6 GW) as well as the ‘open category’ rounds in central government auctions in which no counterfactual LCR auction took place (around 4.6 GW), such as the 100 MW auction in Uttar Pradesh in Batch III.
We consider our dataset with 277 bids complete in terms of auction rounds, since LCRs were abolished on 14 December 2017 due to a ruling of the WTO, with NTPC’s 250 MW Indian-wide auction being the last one under LCR regulation (the auction was later cancelled due to the negotiated phase-out of LCR). For further analysis, we consider the available submitted bids, rescale those to 2014 US dollar values to reflect inflation, and use logged bid values in our regression to normalize them. In summary, we consider bids with a total capacity of 21.7 GW, of which 18.7 GW were submitted in the open category and 3 GW under the LCR scheme.
We also collect detailed firm data for all 85 firms within our sample. For each firm, we analyse whether it belongs to a bigger firm. Several firms are so-called special purpose vehicles, which are created merely to bid in a given auction. Given that these firms have access to the human, financial and technical capital of the bigger firm that they belong to, we use the firm characteristics of the parent company. In addition, we collect data on the employment numbers (which could be found for all firms, as opposed to sales numbers, which were unavailable for many privately owned firms), check whether the firm is an SOE and research whether the firms themselves have manufacturing capacities (that is, are vertically integrated). We analyse whether the firm had already bid in the first phase of the NSM, which might give firms a distinct advantage over newcomers due to experience with local regulations. We also check whether the main focus of the company is energy or whether it has just recently diversified its firm activities into energy. Lastly, we analyse whether the firm was founded in India or was registered abroad. We posit that all of these characteristics may influence whether a firm participates in a given auction (for example, we assume that firms that have local manufacturing capabilities are more likely to participate in LCR auctions).
We used solar irradiation maps from the National Renewable Energy Laboratory (NREL) and converted them via QGIS (version 2.8.14) into mean, maximum and minimum values for each state. The NREL dataset provides solar resource in India for surface cells of 0.1 degrees in both latitude and longitude, or nominally 10 km in size. The NREL calculations are based on data from the Meteosat-5 and Meteosat-7 geostationary meteorological satellites42.
Patent data were collected from the Indian Patent Database using web scraping methods (Python package Selenium), as the patent office does not offer an application programming interface (API). We employ a typology from a recent, comprehensive review of international patent classification (IPC) terms and their correspondence to PV system components published in Renewable and Sustainable Energy Reviews49. This typology covers 284 distinct IPC codes in seven groups: cells, panels, electronics, energy storage, monitoring/testing, devices and combined. Studies comparing global trends in patenting to track innovation normally rely on large patent databases such as the European patent database PATSTAT, which aggregates patent statistics across many domestic offices. However, for India the PATSTAT data are woefully incomplete, leading us to resort to web scraping techniques.
The data on domestic production and imports in Fig. 4c are based on the Directorate General of Trade Remedies investigation on the imposition of safeguards on solar PV cells and modules on behalf of five Indian solar producers. The export data in Fig. 4d were exported from the global United Nations trade database Comtrade using the commodity code ‘HS 854140’, which describes ‘photosensitive semi-conductor devices, including photovoltaic cells whether or not assembled in modules or made up into panels’50. More

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