Ecology

Investigation method
From March 2015 to December 2018, we surveyed 36 important tributaries of the TGR (Fig. 3) and conducted an investigation of macroinvertebrates. For the sake of convenience, we labeled tributaries from the reservoir dam to its tail area sequentially as R01–R36, i.e., R01 (Xiangxi River), R02 (Qinggan River), R03 (Shennong River), R04 (Baolong River), R05 (Guandu River), R06 (Daning River), R07 (Daxi-F River), R08 (Caotang River), R09 (Meixi River), R10 (Changtan River), R11 (Modao River), R12 (Tangxi River), R13 (Xiao River), R14 (Zhuxi River), R15 (Rangdu River), R16 (Ruxi River), R17 (Huangjin River), R18 (Dongxi River), R19 (Chixi River), R20 (Long River), R21 (Bixi River), R22 (Quxi River), R23 (Zhenxi River), R24 (Wu River), R25 (Lixiang River), R26 (Longxi River), R27 (Taohua River), R28 (Yulin River), R29 (Wubu River), R30 (Changtang River), R31 (Chaoyang River), R32 (Jialing River), R33 (Huaxi River), R34 (Yipin River), R35 (Daxi-J River), and R36 (Qi River).
Figure 3

Schematic diagram of important tributaries of TGR and sampling points (plotted by ArcGIS 10.5, https://www.32r.com/soft/16101.html).

Full size image

This study was approved by the Environmental Protection Bureau of the Three Georges Reservoir.
A total of 175 sampling points were set up in all tributaries. Four parallel samples were taken from each sampling point. At least one sample was taken from each microhabitat (mainly including four microhabitats, i.e., shoal, deep pool, pebble and aquatic habitat). Parallel samples from the same sampling point were mixed together. The quantitative and qualitative sample collection methods were combined in this study. The quantitative collection was performed first, and then the qualitative collection for the same sampling point. The qualitative samples were collected by D-net. Quantitative samples of wadable sampling points were collected using a Surber net with an area of 0.3 m × 0.3 m. Quantitative samples of non-wadable sampling points were collected using a D-net with a bottom side length of 0.3 m. The collected samples were put into sample bottles (bags) and fixed with 5% formaldehyde solution. Then the samples were identified and classified under the laboratory conditions.
Selection of reference sites and impaired sites
A reference site refers to a sampling point with no or little anthropogenic disturbance, while a impaired site refers to a sampling point subject to obvious anthropogenic disturbance29. A total of 15 reference sites and 160 impaired sites were selected from 175 sampling points based on anthropogenic disturbance, vegetation coverage, population distribution, and the distribution of industry and agriculture in the vicinity of the sampling site6,8 (Table 7, Supplementary Fig. 1).
Table 7 Assessment criteria for reference sites and impaired sites and the assessment outcomes.
Full size table

Creation and selection of the assessment metric index system
With reference to the river health assessment indexes in China13,15,18,24, North America6,24 and Europe5, and based on the ecological characteristics such as species composition and abundance, sensitivity, tolerance and functional feeding groups, we constructed 26 candidate metrics (Table 8) for B-IBI. These candidate metrics have significant or noticeable response to human activities, and normally, can be applied to relatively large geographic areas; therefore, they can be used to indicate the ecological quality of rivers6,23,24. Among these metrics, 17 were associated with species composition and abundance, which included the total number of taxon, the number of EPT taxa, the number of crustacean and mollusca taxa, the number of ephemerida taxa, the number of pteroptera taxa, the number of trichoptera taxa, the number of diptera taxa, the number of chironomidea taxa, the percentage of EPT, the percentage of crustacean and mollusca, the percentage of ephemerida, the percentage of pteroptera, the percentage of trichoptera, the percentage of dipteral, the percentage of chironomidea, the percentage of oligochaeta and the Shannon-Weiner diversity index. Species composition and abundance-related indexes reflect the diversity of macrobenthic communities. An increase in species diversity is associated with the improvement of community health, which indicates that the niche space and food sources are sufficient to support the survival and reproduction of multiple species. The candidate metrics related to sensitivity and tolerance in this study were the number of sensitive taxa, the number of tolerant taxa, the percentage of dominant species and the percentage of the top three dominant species. Different zoobenthos show different degrees of sensitivity and tolerance to the influencing factors in the river habitat, for which these characteristics can be used to assess the health status of the river. In addition, the taxa and percentage of functional feeding groups are closely associated with their living environment, and the parameters that were used to represent functional feeding in this study were the percentages of shredders, herbivores, filterers, scrapers and predators. Some of the representative images of the identified taxa were shown in Supplementary Fig. 1E,F.
Table 8 Candidate parameters for B-IBI and their response to anthropogenic disturbance.
Full size table

The selection of core metrics for B-IBI mainly includes three steps: analysis of distribution range of candidate metrics, analysis of discriminant ability of candidate metrics and analysis of correlation between candidate metrics23.
Analysis of distribution range of candidate metrics
According to the numerical value of each biological metric in the reference site, an initial analysis was conducted to exclude the following two types of metrics: metrics with excessive nought values, which did not meet the requirement for a universal applicability; metrics with a scatter value distribution, and a standard deviation greater than or equal to the mean, indicating that the standard deviation of this value was relatively big and unstable, thereby unsuitable to be used as biological metrics6.
Analysis of discriminant ability of candidate metrics
After analyzing the distribution range of candidate metrics, those unsuitable for biological evaluation were eliminated. The distribution of the remaining eligible metrics for the reference site and the impaired site was analyzed using the box-plot, to mainly compare the distribution range of the 25th quantile to the 75th quantile of the reference site and the impaired site and the overlap of “box” InterQuartile Range (IQR), and judge which biological metrics could best distinguish between the reference site and impaired site. An IQ value ≥ 2 indicates a small overlapping part between the reference site and the impacted site, which means a significant difference in the related parameter between the reference site and the impacted site, suggesting a noticeable response to human activity6,24. The IQ scoring criteria were as follows6,24: 3 point, no overlapping between the two box bodies; 2 points, the box bodies have a small part of overlapping, but the median of neither body falls within the limits of its counterpart; 1 point, most parts of the box bodies overlap, and the median of at least one box body lies within the limits of its counterpart; 0 points, one box body falls within the limits of the other, or the medians of each body are within the other’s limits.
Correlation analysis of candidate metrics
Pearson correlation analysis was further performed of the metrics that met the preliminary conditions. If the correlation coefficient (left| {text{r}} right|) between two metrics is greater than 0.75, and they are intrinsically linked. Then most of the information reflected is overlapping. Therefore, it is OK to select one of them. If no intrinsic connection is found between two metrics, then both metrics can be selected even if the correlation coefficient is greater than 0.758.
After screening through the above three steps, core metrics of the B-IBI are finally determined.
Construction of B-IBI
The core biological metrics screened out by the above method were used as the metrics for final biological assessment. The metrics used for biological assessment were standardized using the ratio scoring method, to unify the evaluation metric23.
(1)
For a metric that decreased with increasing interference, the metric was normalized by dividing the value of this metric at each sample point with the 95% quantile of all sample points:

$${text{V}}_{{text{i}}}^{prime } = {text{V}}_{{text{i}}} /{text{V}}_{{{95}% }} ;$$

(2)
For a metric that increased with increasing interference, the metric was normalized by using the 5% quantile of this metric at all sample points as the reference object:

$${text{V}}_{{text{i}}}^{prime } = left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{text{i}}} } right)/left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{{5}% }} } right),$$

where Vi′ is the normalized value of the metric at the ith sampling point; Vi the actual value of the metric at the ith sampling point; V95% the 95% quantile of the metric; V5% is the 5% quantile of the metric; VMAX is the maximum value of this metric in all sampling points. The health thresholds of 5% quantile and 95% quantile can eliminate extreme abnormal values and retain most of biological information.

B-IBI assessment criteria
The 95% quantile of B-IBI distribution of all the sections/tributaries used for the health threshold can eliminate extreme abnormal values and retain most biological information. The distribution range lower than this value is divided into four portions, and the quartile close to the 95% quantile indicates a small disturbance. The biological integrity grade and the corresponding range of IBI6 are determined according to the 95% quantile and the quartile value, and the section/river health was classified into five grades, namely, excellent, good, fair, poor and very poor. More

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Study animals
Between June and August 2014, male and female Gerris latiabdominis were collected using insect nets from small ponds and an old swimming pool at Seoul National University, Seoul, South Korea. The number of water strider collected weekly varied depending on the current experimental requirements. In total, we collected near 100 individuals and used 62 of them in the experiments reported here. Collected water striders were housed in plastic containers (52 × 42 × 18 cm, 2–4 individuals/container) with aerated water, foam resting platforms, and two frozen large crickets per container per day. Each water strider’s thorax was marked with three unique color-coded dots using enamel paints. Females and males were housed separately.
Experiments
Effect of weight addition on the performance of first jumps
Experimental design is graphically summarized in Fig. 1b (also see Supplementary Materials PART 4). To determine the effect of increase in body mass on the behaviour, we tested water striders in two conditions of the Additional weight treatment: weight-added (11 females and 16 males) and weight-not-added (20 females and 15 males). After measuring the weight with an Ohaus electronic scale with the precision of 0.1 mg, the water striders were randomly assigned to either of the treatment group. In weight-added group, a flat coiled aluminum wire (~ 7.5 mg weight in males, ~ 10.5 mg weight in females) was secured to the backs of water striders with a tiny drop of non-water soluble glue gel (applied only on top of thorax). Added weights caused an increase of body mass by about 50% (54.5% ± 9.2 (mean ± SD) in males and 52.8% ± 5.7 in females). The body weight of a male is about 70% of female body weight on average (similar based on median or average body weights). Preliminary theoretical calculations using the model of surface-tension dominated jumping3 suggested that an average female with the extra weight equivalent to the average male body mass would be able to jump and to achieve take-off velocity of about 0.75 m/s for the leg angular velocity of about 40 rad/s. However, based on our observations, when a male sits on the female’s back during copulation and mate guarding the male’s hindlegs are always on the water, probably adding to the support for the mating pair on the water surface. The tips of male midlegs can also be on the water surface, possibly also helping in support on the water surface. All evidence suggests that the male’s support on the water surface contributes to some extent to the forces maintaining the mating pair on the surface of water. Hence, the female does not perceive the full body weight of the mating male. Our preliminary trials with additional weight of different masses indicated that the weight similar to the male body mass is too heavy for the purpose of our experiments because some females were not able to stay on the surface for extended time periods. This was not observed for the extra weights used in our experiments.
After the weight was added to the water strider, the animal was allowed to rest with 2–3 other individuals of the same sex in a container filled with water (20 × 14 × 10 cm). After three hours, the water strider was placed in a box where the 3-D slow motion movie of the jump was recorded (labeled as the First jump; see below for the details). In weight-not-added group the individuals were treated similarly and handled for similar duration but no extra weight (neither wire nor the glue) was put on their backs. Triggering repeated jumps successively many times in the small container in which they were filmed likely leads to changes in performance due to repeated jumps within relatively short time and due to accumulated effect of the heat of the lights needed for high speed filming. We decided to use the design in which we took one jump per individual. The final sample sizes differ between treatments because some movies were discarded at the analysis stage for technical reasons.
Effect of jumping experience on adjustment of jumping performance
In order to test the effect of individual’s experience on adjustments of jumping behaviour we subjected the males and females from the preceding First jump to two conditions of Jumping experience [JE] treatment: presence and absence of frequent jumping during a three-day period (Fig. 1). For three days following the filming of First jump, water striders were kept in groups of 3–4 individuals per container (20 × 14 × 10 cm; filled with aerated water) and fed two frozen crickets per day. Each container was assigned to either JE-present or JE-absent treatment. In the former, we used an aluminum wire bent in the shape of a hook to touch or poke the insect’s underside in order to trigger 3–5 jumps/hour over 5 h/day. Jumps provide individuals with repeated experience of their jumping performance and the opportunity to adjust jumping behaviour. In the latter, individuals were not exposed to these procedures. At the end of the three days, the jumps (Second jump) were recorded in the same manner as for First jump. Sample sizes (listed in caption to Fig. 3 and in Supplementary Table 7 in Supplementary Materials PART 4) differ between treatments because some movies were discarded for technical reasons (see below) and some animals escaped or died.
High speed filming of jumps
We used three synchronized high-speed cameras (FasTec Troubleshooter Model #: TS1000ME), with lens axes perpendicular to one another (Supplementary Fig. 2b in Supplementary Materials PART 4). Lights (Photon Super Energy Light, Aurora CCD-250 W, and PLTHINK Photo Light Think with Metal Halide bulbs) were placed directly opposite to each camera lens (Supplementary Fig. 2b). At the center of the setup was a 10 × 10 × 10 cm clear Plexiglas box filled with water. The jumps were invoked by an aluminum wire bent in the shape of a hook underneath the water surface. Jumps were recorded at 500 frames per second. Clips with insects that were accidentally pushed upward by the wire were excluded from the analyses. Examples of jumps extracted for the movies are shown in the Supplementary Movie.
Variables extracted from the videos
We tracked the locations of body parts of water striders frame by frame in a three dimensional x, y, z, coordinate system (x, y are horizontal axes, z-coordinates are on the vertical axis, and origin is located at the level of undisturbed water surface) using video tracking software MaxTRAQ 3D (Innovision Systems). We tracked three markers; body center (defined as point between midleg and hindlegs), right midleg dimple depth, and left midleg dimple depth. Dimple depth is the deepest point of water surface deflection under the pressure from a midleg. From this data we calculated upward (vertical) and forward (horizontal) body velocities. For each pair of consecutive frames, we calculated the raw upward velocity of body center (along the vertical axis z) by dividing the vertical shift of body center (vertical distance between z coordinates of body center in the two consecutive frames) by the duration (2 ms between frames in 500 fps movie). Then, we calculated smoothed vertical velocity (m/s) by using rolling three-point average of three successive velocities. In an analogical manner we calculated the values of smoothed horizontal velocity (m/s) during a jump. From the data we extracted four variables used in analyses:
Angular leg speed (rad/s): Legs move downward as a result of downward angular femur movement powered by insect’s muscles, and the rotational rate of the leg downward movement is termed Angular leg speed (rad/s). To match the angular leg speed calculations in the theoretical model3, we calculated the Angular leg speed in several steps using empirical data and theoretical formulas from the existing model3. The coordinate system included vertical axis (z) with origin (z = 0) at the level of undisturbed water surface. First, for each frame we calculated average dimple depth as an average z from the left and right dimple depths’ z values, and the downward leg reach as the distance between body center’s z and the average dimple depth. Then, for each pair of consecutive frames, we calculated the downward velocity of dimple depth relative to body center (along the vertical axis z) by dividing the change in the downward leg reach between two consecutive frames by the duration (2 ms between frames in 500 fps movie). By using rolling three-point average from three successive downward velocities we obtained smoothed leg speed (m/s). Finally, we calculated the maximal downward speed of legs vs,max (m/s) as an average from the three largest smoothed velocity values. The downward Angular leg speed (ω) was calculated according to Yang et al.3 by approximation starting from the previously approved formula3 for the maximal downward speed of legs vs,max containing leg length ll: (v_{s,max } approx omega *left( { l_{l} – y_{i} } right)*sin left( {2omega t} right)) (yi indicates distance from the surface to insect body at rest and t indicates time during jump). See “Physical constraint from water surface: theoretical upper threshold of performance” below for more details about the model. The calculations resulted in the variable (Angular leg speed) that was directly relevant to the theoretical predictions of the optimal jumping behaviour3.
Take-off angle (deg): We defined take-off angle (deg) as the angle of trajectory to the water surface when the water strider leaves the surface of water. Takeoff angle was calculated from the ratio of horizontal and vertical vectors of the smoothed body center velocities.
Take-off velocity (m/s): Take-off velocity (m/s) is the vertical velocity of body center when the water strider leaves the surface of water. We determined the moment of leaving the water surface as the frame when legs disengage from the surface. Vertical velocity indicates how fast the animal removes itself from surface of water. A high take-off velocity is important when predators attack from underneath the water surface. This variable is a crucial component of the theoretical model of optimal jumping performance by water striders3.
Meniscus breaking (binary): Sometimes jumping water striders break the water surface. When left or right midleg pierced the water surface by more than a quarter of its full leg length the jump was categorized as a jump with meniscus breaking-present. Otherwise the jump was categorized as a jump with meniscus breaking-absent.
Statistical analyses
Effect of weight addition on jumping performance—To analyze the effect of Additional weight on jumping performance of First jumps, we used Wilcoxon rank sum tests (Mann–Whitney test) to compare weight-added with weight-not-added groups for each sex separately. We used nonparametric statistical methods here because of small sample size that does not allow to confirm the parametric methods’ assumptions with high reliability (nevertheless the tests indicated that the parametric assumptions were probably met and in Supplementary Materials PART 1we also provide results from parametric comparisons: t-tests and Welch’s t-tests. In order to investigate whether Additional weight effect is statistically significantly different between sexes we switched to parametric analyses and run two-way ANOVA tests including the interaction effect between two independent variables (Additional weight and Sex) separately for the three dependent variables: Angular leg speed, Take-off angle and Take-off velocity.
Effect of jumping experience on adjustments of jumping performance—For each individual, we calculated three indices of adjustment (change) in performance between First and Second jumps. For each of the three dependent variables, we subtracted the value at First jump from the value at Second jump (for analysis of Second jump solely—see Supplementary Materials PART 3). Linear regression model was used to investigate the effect of Jumping experience and Additional weight treatments on jumping adjustments. Because of the small sample sizes, estimates and 95% confidence intervals of regression coefficients were reported using nonparametric bootstrap procedure with 10,000 replications of each linear model (‘boot’ package in R).
Meniscus breaking—To analyze the effect of Jumping experience and Additional weight treatments on the probability of breaking of the water surface (Meniscus breaking-present) we used Fisher’s exact test. To determine the effect of Meniscus breaking on Take-off velocity we used Wilcoxon rank sum test separately for males and females. All statistical analyses were performed using R (version 3.3.2;43).
Physical constraint from water surface: theoretical upper threshold of performance
During a water strider’s jump, the water surface can be pushed downward only so much before breaking. Thus, a theoretical upper threshold of performance exists. The mathematical model of surface tension dominated jumping3 allows to predict the moment of surface breaking and the optimal behaviour of vertically jumping water striders without surface breaking. The model contains a non-dimensional variable: ΩM1/2. Its value depends, among others, on the Angular leg speed used by water striders during jump and on morphology: body mass and midleg’s tibia and tarsus length—the leg parts on which water strider’s body is supported on the water surface. The theory predicts that for a given total length of midlegs there is a threshold value of ΩM1/2 above which surface breaking will occur and the jump will be inefficient. We determined if water striders used Angular leg speed values that resulted in theoretical values of ΩM1/2 below this critical threshold. In order to more precisely predict the theoretical threshold value of ΩM1/2we modified the original model3. The original model used a simple average length of all four legs (mid-legs and hind-legs) and did not reflect a difference between the length of hind and mid legs. We changed the original equation into systems of differential equations using information about midlegs and hindlegs separately. Modified predicted threshold values of ΩM1/2 were compared with empirically observed values of ΩM1/2in order to determine if the observed adjustments of leg speed by water striders lie within the theoretical limit of performance set by physical properties of water. We used the same parameters as in3 for, lc, capillary length, g, gravitational acceleration, ρ, density of water. Because of the short length of legs, we approximated C, flexibility factor, as 1. Downward angular velocity of leg rotation, ω, was calculated by approximation, (v_{s,max} approx omega {Delta }lsin left( {2omega t} right)). The averaged length of femur, tibia and tarsus were measured from 24 individuals of each sex in G. latiabdominis and yi, the distance from body center to the undisturbed water surface in the resting position of the water strider was measured from 4 movie clips of each sex. Measured variables were averaged (Supplementary Table 8 in Supplementary Materials Part 4) and used to determine lt, average length of tibia plus tarsus, ll, average leg length, and Δl = ll − yi, maximal reach of the leg. Note that, the average length of tibia plus tarsus of hind and mid legs, lth, ltm, and the average length of maximal reach of leg of hind and mid legs, Δlh, Δlm, can be represented as:

$$l_{th} = 0.77l_{t} ,l_{tm} = 1.23l_{t} ,Delta l_{h} = 0.84Delta l,Delta l_{m} = 1.16Delta l$$

$$ frac{{d^{2} H_{m} }}{{dleft( {omega t} right)^{2} }} + frac{4 cdot 1.23}{{{Omega }^{2} M}}H_{m} left( {1 – H_{m}^{2} /4} right)^{1/2} + frac{4 cdot 0.77}{{{Omega }^{2} M}}H_{h} left( {1 – H_{h}^{2} /4} right)^{1/2} – 2 cdot 1.16Lcos left( {2omega t} right) = 0, $$
(1)

$$ frac{{d^{2} H_{h} }}{{dleft( {omega t} right)^{2} }} + frac{4 cdot 1.23}{{{Omega }^{2} M}}H_{m} left( {1 – H_{m}^{2} /4} right)^{1/2} + frac{4 cdot 0.77}{{{Omega }^{2} M}}H_{h} left( {1 – H_{h}^{2} /4} right)^{1/2} – 2 cdot 0.84Lcos left( {2omega t} right) = 0, $$
(2)

where (H_{m} = h_{m} /l_{c} ) is the dimensionless dimple depth of mid legs (hm, dimple depth of mid legs), (H_{h} = h_{h} /l_{c}) is the dimensionless dimple depth of hind legs (h1, dimple depth of hind legs), ({Omega } = omega left( {l_{c} /g} right)^{1/2}) is the dimensionless angular velocity of leg rotation, (M = m/left( {rho l_{c}^{2} Cl_{t} } right)) is the dimensionless index of insect body mass (m, insect body mass), and (L = {Delta }l/l_{c}) is the dimensionless maximum downward reach of leg. The variable ΩM1/2 is calculated, as the name suggests, by multiplying the above-defined Ω by square root of M3. For a given L, the optimal value of ΩM1/2 for maximal take-off velocity is achieved when the maximal hm is equal to the critical depth, (sqrt 2 l_{C}), just before meniscus breaking. Wetted leg length, ls, was measured from 24 individuals of each sex in G. latiabdominis and initial height, yi, was measured from 12 recorded videos of females (6 individuals) and 9 recorded videos of males (5 individuals) (Supplementary Table 8 in Supplementary Materials Part 4). The ode45 function in Matlab was used to solve Eqs. (1) and (2) to get the optimal ΩM1/2 of male and female water striders (red lines in Fig. 4). More

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Bacteriophages
Four lytic E. coli-specific phages were used in the present study: MS2 (DSM 13767), T4 (DSM 4505), T7 (DSM 4623), and ϕX174 (DSM 4497). The genomic and structural properties of the phages as well as their bacterial hosts are listed in Table 2. For preparation of the virus isolate stocks, the respective bacterial host was grown in sterile LB medium (LB broth Miller, Sigma-Aldrich, St. Louis, Missouri) until an optical density of 0.3 measured at 600 nm was reached, then inoculated with phages at a virus-to-bacteria-ratio of 0.1, followed by overnight incubation. Remaining bacterial cells were killed by the addition of 1/10 volume of chloroform for 1 h. After separation from the bacterial cell debris, virus stocks were filtered with 0.22 µm syringe filters (Millex-GP, Merck-Millipore, Billerica, Massachusetts) and filtration was repeated prior preparation of samples for measurements.
Environmental samples
Environmental samples were collected from four different aquatic habitats: the income water tank of a wastewater treatment plant (Gut Großlappen, Munich, Germany), an on-site groundwater collection well (48°13′25.8″ N 11°35′45.4″ E, Munich, Germany), a lake (Feldmochinger See; 48°12′56.0″ N 11°30′49.4″ E, Munich, Germany), and a river (Isar; 48°32′59.3″ N, 12°10′42.4″ E, Landshut, Germany). To remove particles the size of bacteria and larger, all water samples were filtered with 0.22 µm syringe filters (Millex-GP). Measurements with flow cytometer and nanoparticle tracking analysis were performed simultaneously and on the sampling day. Quantification with epifluorescence microscopy as well as DNA extraction was conducted on the next day. Samples were stored in 4 °C.
Additionally, a mixed water sample (lake and wastewater) with an approximate concentration of 108 virus-like particles per mL (VLP mL–1) was prepared. This sample was spiked with 1× 108, 5× 108 and 1× 109 T4 particles mL−1. Before the addition, phage T4 stock has been quantified with qPCR.
Viral quantification
All measurements were performed in biological and technical duplicates.
Plaque assay (PA)
The PA was performed using a soft agar overlay technique as described elsewhere24. Briefly, 0.5 mL of appropriate dilutions of phages were mixed with an equal volume of fresh cultures of the corresponding hosts, grown overnight (incubated in LB medium at 37 °C until an optical density of 0.3 measured at 600 nm was reached). The phage-bacteria-suspension was mixed with 3 mL warm soft agar (0.75% w/v agar and 2.5% w/v LB) and gently poured on a petri dish already containing an LB agar layer (1.5% w/v agar and 2.5% w/v LB) in biological and technical replicates. Upon solidification, the petri dishes were incubated bottom up for overnight at 37 °C. After 15–20 h, depending on the bacterial growth efficiency, the plaques formed were manually counted and the phage titers as plaque-forming units per mL (PFU mL–1) were calculated.
Flow cytometry (FCM)
All samples were prepared as described previously with some adaptations14. We decided on these modifications based on the publications of Tomaru and Nagasaki (2007) and Brum and colleagues (2013). More precisely, samples were not fixed with glutaraldehyde after sampling as this may decrease the fluorescence intensity as well as the viral counts. Tomaru and Nagasaki concluded, that a fixation does not necessarily improve the staining ability of the virus particles20. Besides, our samples were measured immediately on the day of sampling, thus a preservation of the viral particles was not necessary. Another step recommended by Brussaard (2004) we did not follow is the flash freezing of the viral sample in liquid nitrogen. It has been shown that nitrogen fixation hampers the preparation procedure for TEM resulting inter alia in morphology changes25. To what extent particles would be enumerated correctly after fixation and nitrogen treatment with nanoparticle tracking analysis where particle integrity would certainly play a role during the enumeration process, is also debatable. As consequence, we decided, to omit this step in order to maintain a consistent sample handling and accomplish comparable conditions for all methods.
In brief, samples were diluted appropriately with sterile, filtered PBS buffer (0.02 µm Anotop 25 syringe filter, Whatman, Maidstone, UK; Sigma Aldrich) to fulfill the instrument’s optimal concentration requirements of approximately 106 VLP mL–1 (Table 1). Fluorescent TRUCOUNT beads (BD, Becton, Dickinson and Company, Franklin Lakes, New Jersey) were added to each sample as an internal reference. The samples were stained with 1 × SYBR gold nucleic acid stain (Thermo Fisher, Waltham, Massachusetts) and incubated either for 10 min at 80 °C (FCM80) or for 1 h at 30 °C (FCM30) prior to measurement. Tomaru & Nagasaki recommended an incubation at room temperature, as higher temperatures reduced the viral counts. We chose therefore two staining temperatures, one at 80 °C, following the suggestion of Brussaard14 and one at 30 °C, following the reference of Tomaru & Nagasaki20.
All samples were measured with a FC500 flow cytometer equipped with an air-cooled 488 nm Argon ion laser (Beckman Coulter, Brea, California) in biological and technical replicates. Analysis and evaluation of the samples was performed using StemCXP Cytometer software (v2.2).
Nanoparticle tracking analysis (NTA)
Viral isolate samples were diluted appropriately with sterile phage buffer (10 mM Tris [pH 7.5], 10 mM MgSO4, and 0.4% w/v NaCl) to obtain the optimal concentration range of 107–109 VLP mL–1 (Table 1). Afterwards, samples were either untreated or stained with 1 × SYBR gold for 10 min at 80 °C or 1 h at 30 °C (NTA80 or NTA30, respectively). Each sample was injected manually into the machine’s specimen chamber with a sterile 1 mL syringe (Braun, Melsungen, Germany), and measured three times for 20 sec at room temperature in three independent preparations. Samples were measured using a NanoSight NS300 (Malvern Pananalytical Ltd., Malvern, United Kingdom) equipped with a B488 nm laser module and a sCMOS camera, following the manufacturer’s protocol. Analysis was performed with the NTA 3.1 Analytical software (release version build 3.1.45).
Epifluorescence microscopy (EPI)
Staining of the samples was carried out as described by Patel et al.26. Briefly, all samples were diluted appropriately with 0.02 µm filtered 1 × TE buffer (pH 7.5, AppliChem, Darmstadt, Germany) to a concentration of 107 particles mL–1. For environmental samples with lower concentrations, a volume of 10 mL was used.
Then, 1 mL of each diluted sample (10 mL of environmental samples) was passed through a 0.02 µm Anodisc filter (Whatman) in duplicates. After complete desiccation, the filter was stained using a drop of 2 × SYBR gold dye (Thermo Fisher) with the virus side up, and incubated at room temperature for 15 min in the dark. Stained filters were mounted on a glass slide with 20 µL antifade solution (Thermo Fisher). Slides were analyzed using an Axiolab fluorescence microscope (Carl Zeiss, Oberkochen, Germany) equipped with a 488 nm laser. A camera was used to take ten pictures per sample, which were analyzed using ImageJ (version 1.50i). Numbers of particles on the whole filter were calculated by multiplying the counts with the quotient of the area of the filter by area of the pictures.
Quantitative real-time PCR (qRT-PCR)
Prior to the DNA extraction 1 mL of sample has been treated with DNase as described previously with a modified incubation procedure for one hour at 37 °C27. The DNA extraction has been conducted from the complete volume after DNase treatment using the Wizard® PCR Preps DNA Purification Resin and Minicolumns (Promega, Madison, Wisconsin) as previously described28. RNA was extracted with a QIAmp MinElute Virus Spin Kit (total volume of 1 mL sample) (Qiagen, Hilden, Germany) and cDNA was synthesized using a DyNAmo cDNA Synthesis Kit (Thermo Fisher) according to the manufacturers protocols. For all samples, DNA or RNA was isolated in duplicates.
T4 was quantified using primers amplifying a 163 bp region of the gp18 tail protein (T4F 5′-AAGCGAAAGAAGTCGGTGAA-3′ and T4R 5′-CGCTGTCATAGCAGCTTCAG-3′)29. For T7, primers amplifying a 555 bp segment of gene 1 (T7_4453F 5′-CTGTGTCAATGTTCAACCCG-3′ and T7_5008R 5 ‘-GTGCCCAGCTTGACTTTCTC-3′)30. ϕX174 was quantified using primers specific for the capsid protein F (ϕX174F 5′-ACAAAGTTTGGATTGCTACTGACC-3′ and ϕX174R 5′-CGGCAGCAATAAACTCAACAGG-3′) resulting in a 122 bp fragment31. For MS2, primers amplifying a 314-bp fragment (MS2_2717F 5′-CTGGGCAATAGTCAAA-3′ and MS2_3031R 5′-CGTGGATCTGACATAC-3′) were used32. Quantitative PCR was performed in a total volume of 20 µL consisting of 10 µL Brilliant III Ultra-Fast QPCR Master Mix (Agilent, Santa Clara, California), 5 µL DNA template or PCR-grade water as a negative control, as well as the following optimized primer concentrations (supporting information): 0.5 µM primers T4F and T4R, 0.8 µM primers T7_4453F and T7_5008R, 0.6 µM primers ϕX174F and ϕX174R, or 0.3 µM primers MS2_2717F and MS2_3031R, respectively. The amplifications were run on a Mx3000P qPCR system (FAM/SYBR® Green I filter [492 nm–516 nm], OS v7.10, Stratagene, San Diego, California) with the following cycling conditions: T4: 95 °C for 10 min, (95 °C for 15 sec, 60 °C for 1 min, 72 °C for 1 min) for a total of 45 cycles, T7: 95 °C for 12 min, (95 °C for 30 sec, 58 °C for 30 sec, 72 °C for 1 min) for a total of 30 cycles, ϕX174: 94 °C for 3 min, (94 °C for 15 sec, 60 °C for 1 min) for a total of 40 cycles, and MS2: 95 °C for 10 min, (95 °C for 15 sec, 50 °C for 30 sec, 72 °C for 30 sec) for a total of 45 cycles. Each replicate was measured four times. Analysis of the melting curves confirmed the specificity of the chosen primer as no variations compared to the standard melting curves could be observed. Standard curves were prepared using the appropriate dilutions of gblocks gene fragments (IDT, Coralville, Iowa) of the respective viral DNA in PCR-grade water (supporting information, Tables S1 and S2). Data analysis was performed using the manufacturer’s MxPro Mx3000P software (v4.10).
TEM preparation
Although TEM may be used for quantification, only the virus morphology and integrity upon applying the staining conditions were monitored. Therefore, the phages MS2 and T7 were either incubated for 10 min at 80 °C or further processed without any temperature treatment. Ten µL of the sample were then applied to the carbon side of a carbon-coated copper grid. Excessive water was blotted dry with a filter paper and washed two times with double-distilled water. After each washing step grids were again blotted dry onto a filter paper before negative staining with 2% uranyl acetate for 20 sec. The staining liquid was blotted onto a filter paper and the grids were air-dried as described previously33. Transmission electron microscopy was carried out using a Zeiss EM 912 with an integrated OMEGA filter in zero-loss mode. The acceleration voltage was set to 80 kV and images were recorded using a Tröndle 2 k × 2 k slow-scan CCD camera (Tröndle Restlichtverstärker Systeme, Moorenweis, Germany).
Sample stability test
In order to substantiate our decision of omitting a fixative step for FCM measurements and to confirm a certain stability of the virus concentration over a short time range (few days), phage T4 and wastewater samples were measured with FCM at time 0, after 24 h and after 48 h. The samples were either kept in 4 °C or were fixed with 0.5% glutaraldehyde for 30 min in 4 °C followed by freezing in liquid nitrogen with adjacent storage at -80 °C, as suggested by Brussaard (2004). At each time point, samples were prepared for FCM as described above with two different staining procedures (30 °C and 80 °C). Additionally, a fixed T4 phage sample was prepared for NTA measurements in the same way in order to test the usability of glutaraldehyde fixation. For phage T4, measurements of the 4 °C, unfixed samples were mostly slightly higher compared to the fixed samples (Fig. S6a,b). Comparing the initial quantification with the results after 48 h, the decrease in counted particles was minor. For the wastewater samples, viral numbers of the unfixed samples were marginally lower, however, a general decline in particle numbers over time could be observed (Fig. S6c,d). This decline was in all cases less than one order of magnitude. As both, fixed and unfixed samples declined only to a small extent and no trend of a stronger decrease of viral particles in the unfixed samples could be observed, omitting the fixation with glutaraldehyde and liquid nitrogen is not supposed to have a wide influence on the enumeration within 48 h.
Statistical analysis
Statistical analysis was carried out in R (v3.4.3) and RStudio (v1.1.383). Data were log transformed and analysis of variance (ANOVA) was conducted. Normal distribution of data was confirmed by density plots and quantile–quantile plots; homogeneity of variances was confirmed with Levene’s test. Afterwards, multiple pairwise comparisons were calculated with a post-hoc Tukey honest significant differences test. In addition, similarities in viral isolate quantification methods were assessed using principal coordinate analysis. More

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