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Different sampling strategies yield different microbial communities
The sampling strategies compared in this study (homogenizing tissue before subsampling and homogenizing tissue after subsampling) are common methods found in the literature for characterizing plant-associated microbial communities14,23,26,29. Procrustes analyses and community overlap between sampling strategies demonstrated that different strategies can capture disparate microbial communities within plants, with the extent of these differences depending on the community targeted and plant tissue type sampled. In FFE as well as bacterial and non-AM fungal communities in roots, subsamples from the same plant resulted in completely different sets of species recovered, illustrating the severe undersampling that is inherent to each of these strategies. With these sampling strategies, we are undoubtedly sacrificing power and accuracy to characterize the subtler aspects of plant microbiome interactions, despite often seeing community differences across landscapes, treatments or seasons.
Richness was higher when homogenizing before subsampling for bacteria only, despite differences observed in composition for all groups. It is perhaps surprising that homogenizing plant tissues before subsampling did not recover more species than homogenizing after subsampling for fungi as well, because with the former approach, more plant tissue is initially represented. Indeed, a previous study showed that sample pooling or homogenizing before subsampling resulted in a higher richness of soil fungi compared to equally sized individual samples50. In Song et al. (2015)50 they also found that multiple individual subsamples, rather than the single homogenized subsample, resulted in higher richness. This may suggest that the scale at which we are physically able to break down the particle size of plant tissues, as opposed to soil, is not always fine enough to sufficiently homogenize the fungi within. Because of this, plant-associated microbial communities may require a greater sampling effort than soil microbes. Additionally, the removal of low-abundant SVs did not result in differences in richness between the two sampling strategies for any microbial group, suggesting that neither strategy is better at capturing rare species. Although this study was performed only on milkweed plants, we believe that these results are applicable to other plant species as well. The richness reported here is similar to other studies of plant-associated microbes (e.g.51,52), indicating that differences in subsamples were not due to extreme richness of milkweed-associated microbes.
Microbial diversity should inform sampling effort
The higher congruency that we saw between sampling strategies for AMF compared to other microbial communities may be due to the differences in their local and global estimated richness. While the global number of AMF species has been estimated in the hundreds to low thousands42,53, global estimates of fungal species in general range in the millions54,55. A recent global estimate of bacterial richness suggests similar scales56. In this study specifically, AMF had the lowest total SV richness and the greatest similarity between sampling strategies, while foliar fungal endophytes had the highest SV richness, and the lowest overlap of SVs between strategies. Since the amount of tissue sampled was equal for all microbial communities, the sampling effort was likely much higher for AMF (relative to the whole AMF community), than it was for bacteria and non-AM fungi. Consequently, with each sample we are likely sampling a much larger proportion of true AMF species richness.
Even though the estimated total community richness was highest for foliar fungi, the average estimated richness per individual plant was highest for AMF. This suggests that similar AMF SVs re-occurred across all plants with low species turnover. On the other hand, fungi in leaves had lower average richness per plant (Fig. 4, Supplementary Fig. S3), but the highest total richness, meaning that there was higher turnover of FFE species among plants sampled. These results may be a direct reflection of the overall community richness of the different microbial groups as well as their ability to spread and co-occur within plants. Based on these patterns, more individual plants and a greater sampling effort within individuals are likely needed to characterize FFE communities compared to AMF communities.
Rare SVs contribute to variation among subsamples
Our results show that low abundant, rare SVs largely contributed to the differences seen between sampling strategies. Even AMF communities, which were already similar, increased in overlap by 50% between strategies after low abundant SVs (represented by  More

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Del Mar methane seep carbonate community analyses
The carbonate rock sample used for microbial community analyses was collected from Del Mar East Seep, Dive SO 177, (32°54.25456764 N, 117°46.9408327 W) at a depth of 1032.06 m. The site is in the northern portion of the San Diego Trough, about 50 km west of San Diego, California. Visible features of the seep include carbonate boulders and pavements colonized by orange and white bacterial mats, possible subsurface methane hydrate (large pits and craters), clam beds, and curtains of methane bubbles [20]. For a more in-depth discussion of the Del Mar Methane Seep, please see Grupe et al., (2015). A small chunk of the rock was sealed in a Mylar bag and shipped on dry ice to the University of Minnesota (Twin Cities). Upon arrival, the carbonate sample was temporarily stored at −80 °C until sampling for DNA extractions. All tools used for sampling biomass were autoclave-sterilized prior to use, and all work was performed in a LabConco A2 biological safety cabinet. Sterile aluminum foil was placed over ice packs to provide a cold and clean working environment. Three tubes were designated for the top of the rock, and three tubes were designated for the bottom of the rock. Biomass was scraped from the respective positions and placed in tubes. Top sample tubes had a biomass weight between 76.5–97.6 mg, and bottom sample tubes had a biomass weight between 30.7–59.3 mg. DNA was extracted from the seep samples using the ZymoBIOMICS DNA miniprep kit (Zymo, Irvine, CA). Nuclease-free water from the kit was processed alongside the samples as a negative control for iTag sequence analyses. The amplification of DNA and the generation iTag libraries of the V4 hypervariable region was performed by the University of Minnesota Genomics Center as previously described [21]. The samples and negative control were sequenced on ¼ of a lane of MiSeq for paired end 2×300 bp reads. Primers and adapters were removed with Cutadapt v. 2.10 [22]. The paired end reads were processed and assembled using DADA2 v1.16.0 [23]. The maximum expected error rate was 2 and reads detected as phiX were removed prior to error detection, merging of pairs and chimera detection. Taxonomic assignment was performed using the Silva database v. 138 [24]. Bioinformatic and statistical analyses was performed using tools in PhyloSeq 1.32.0 [25]. R package Decontam 1.8.0 [26] identified contaminating amplicon sequence variants (ASVs). Thus, ASVs of the genera Escherichia/Shigella, Haemophilus, Streptococcus, and the clade Chloroflexi S085 were bioinformatically removed from analyses. ASVs with statistically different abundances between the top and bottom surfaces were detected with DESeq2 v. 1.28.0.
Scanning electron microscopy of seep carbonates
The carbonate rock sample used for SEM imaging was collected from the Lasuen Knoll Seep (Dive SO 170, 33°23.57489996 N, 118°0.39814252 W) at a depth of 279.705 meters. A small chunk of carbonate rock with filtered bottom water and 25% vol/vol glutaraldehyde was stored at 4 °C, shipped on ice to the University of Minnesota (Twin Cities), and then stored at 4 °C. Several small pieces of the carbonate rock were broken off and placed in an 8-well plate. The carbonate pieces in the 8-well plate were rinsed, and subjected to an ethanol dehydration series as follows: 50% for 2 h, 70% over night, 80% for 15 min, 95% (x2) for 15 min, and 100% (x2) for 15 min. Carbonate samples were then subjected to critical point drying on a Tousimis Model 780 A Critical-Point-Dryer following standard procedures, followed by sputter coating 1–2 nm Iridium in a Leica ACE600 Sputter Coater. Lastly, carbonate samples were visualized at 1.0 kV on a Hitachi SU8230 Field Emission Gun Scanning Electron Microscope.
Continuous flow bioreactor experiments
To investigate the potential for sulfur-oxidizing bacteria to dissolve carbonate minerals, we used flow-through biofilm reactors (herein referred to as bioreactors: CDC; CBR 90–3 CDC Biofilm Reactor). Bioreactors contained eight polypropylene coupon holder rods that each accommodate three 12.7 mm diameter coupons. The lid and coupon holder rods were mounted in a 1 L glass vessel with side-arm discharge port at ~400 mL. A liquid medium (described below and Supplementary Table 1) was circulated through the bioreactor, while mixing was generated by a magnetic stir bar at 80 RPM. Sampling of the coupons was conducted aseptically by removing individual coupon holders and harvesting the coupons, while replacing the removed coupon holder with a sterile rubber plug. Experiments were 21 days in duration. On the final day of bioreactor experiments, one coupon was transferred to a treatment imaging flow cell (herein referred to as flow cell: BioSurface Technologies; Model FC 310 Treatment Imaging Flow Cell) in order to visualize and measure the in vivo pH of the biofilm. The flow cell is an autoclavable polycarbonate plastic cell with a recessed inner circle for installing coupons with biofilms grown in CDC Biofilm Reactors, and is equipped with barbed influent and effluent connectors, and topped with a coverslip for in vivo imaging.
The bioreactor medium consisted of two solutions, salt solution 1 & 2, and six additional components: vitamin solution, trace element solution, yeast extract, sodium bicarbonate, sodium thiosulfate, and sodium metasilicate. One 10 L carboy was prepared by autoclaving 5 L of salt solution 1 and one 10 L carboy was prepared by autoclaving 4.1 L (with an additional 839 mL ddH2O) of salt solution 2 at 121 °C for 80 min. The salt solutions were then aseptically pumped together, followed by the addition of sterile stocks of 10 mL 1000X vitamin solution, 10 mL 1000X trace element solution, 10 mL (1000 g/L) yeast extract, 11 mL 1 M NaHCO3, 10 mL 1 M Na2S2O3, and 20 mL 100 mM NaSiO3 through a 0.2 μM filter.
The final medium had a pH of ~7.85 and contained approximately 1.1 mM HCO3-, 1 mM S2O32-, and 9.14 mM Ca2+, with a saturation state with respect to aragonite (Ωaragonite) of ~0.5, similar to that measured near the sediment/water interface in certain seep environments where carbonate dissolution was observed to occur [7, 27]. The saturation state of aragonite was calculated using CO2SYS_v2.1 [28]. Ingredients for both salt solutions and the additional components added to the medium are presented in Supplementary Table 1. Measured saturation indices throughout bioreactor experiments are presented in Supplementary Table 2.
Mineral preparation for bioreactor experiments
Bulk mineral specimens of aragonite were obtained from D.J. Minerals (Butte, Montana). X-ray powder diffraction (XRD) of the aragonite sample, and peak matching indicates the mineral is nearly 100% aragonite. Samples used for the bioreactor were cored with a diamond coring bit on a drill press, cut to ~1–3 mm thickness and smoothed with a diamond saw. Coupons were then scored with a diamond tipped scribe pen to assign a reference code. After scoring, coupons were placed in a muffle furnace (NEY 2-525 Series II) at 500 °C for 4 h to remove residual organic matter, followed by weighing to 0.01 mg (CAHN 29 Automatic Electrobalance) and stored in a desiccator until being mounted in coupon holder rods.
Assembling the bioreactor
Weighed coupons were mounted in coupon holder rods, and fastened in place with plastic screws to avoid mineral chipping, as occurred with the stock metal screws. The bioreactor lid was equipped with a bubble trap on the media inflow port, a 0.45 μm airport, and a lure-lock covered in foil for inoculating via syringe. The entire bioreactor was autoclaved at 121 °C for 45 min.
Microbial inoculant
Celeribacter baekdonensis strain LH4 was used as the pure-culture inoculant for the bioreactor experiments. C. baekdonensis strain LH4 is a colorless, chemolithoheterotrophic, sulfur-oxidizing bacterium, belonging to the Rhodobacterales within the Alphaproteobacteria. It was isolated from marine sediments collected from a methane seep/brine pool at Green Canyon Block 233 [29] (Lat/Long: 27° 43.4392’ N, 91° 16.7638’ W, Depth: 648 m) in the Gulf of Mexico. Strain LH4 produces acid from thiosulfate oxidation, likely via the sox pathway (soxABCDXYZ) [30]. Strain LH4 also contains other genes related to sulfur oxidation, including four copies of sulfide quinone oxidoreductase ORFs and three copies that encode flavocytochrome c oxidases. Strain LH4 was grown up in the final medium, centrifuged at 10,000 G for 10 min, and washed 2X. OD590 for experiments was on average ~0.07 (~2.3•108 cells/mL based on cell counts and growth curves yielding the equation y = 3•109(x) + 2•107 where y equals cells/mL, and x equals OD590) with an inoculum volume of 20 mL.
Bioreactor experiments
Experiments were run to measure the dissolution of aragonite in a medium with a saturation state of 0.5 with respect to aragonite, similar to that measured near the sediment/water interface in seep environments [27]. Unlike in the sediments where AOM-generated alkalinity is high and carbonate actively precipitates, at the sediment/water interface previous studies have measured a notable drop in alkalinity [11, 31,32,33,34,35,36] and carbonate dissolution was observed to occur [7,8,9, 11, 27]. In total, six continuous -flow bioreactor experiments were run: three biotic experiments under identical conditions at 10 °C, pH ~7.85, 1.1 mM HCO3-, 1 mM S2O32-, and yeast extract; two uninoculated controls run under the same conditions as above to obtain an abiotic dissolution rate in undersaturated conditions; and one biotic experiment was run under identical conditions, minus the addition of S2O32- to elucidate the impact of heterotrophy on mineral stability.
10 L carboys containing the final medium and sterile bioreactors were assembled aseptically in a class A2 biological safety bench. Approximately 325 mL of the media was pumped into the bioreactor, followed by inoculating with 20 mL of pure culture and ~600 μL 1 M HCO3-. The luer-lock was removed and a 0.2 μM filter was added in its place. Bioreactors were then placed in a 10 °C refrigerator on a stir plate at 80 RPM.
Inoculated bioreactors were run in batch phase for 4 h to promote ample attachment of cells to aragonite coupon surfaces. Following the batch phase, the bioreactor was moved back to the clean hood where one coupon holder rod was removed and replaced with a sterile rubber stopper. Two coupons were placed in the incinerator at 500 °C for 4 h, followed by weighing for mass loss. The additional coupon was fixed in 4% PFA for 2 h, washed, and stained with DAPI [4,6-diamidino-2-phenylindole] for 30 min. Stained coupons were washed and mounted on coverslips (0.17 mm thickness) with DPX mountant for cell counting. After batch phase harvesting, the pump was turned on to the max flow rate (31 mL/min) and the first 100 mL out of the reactor was collected to measure pH, alkalinity, [Ca2+], and OD590. pH and alkalinity were measured using a Hanna Instruments total alkalinity mini titrator (HI-84431) and pH meter (HI 1131B) following the manufacturers protocol (Hanna Instruments, Woonsocket, RI, USA). [Ca2+] was measured using a Hach hardness test kit (product #2063900) following to the manufacturer’s protocol (Hach, Loveland, CO, USA). OD590 was measured on a Thermo Scientific Spectronic 20D + (Thermo Fisher Scientific, Waltham, MA, USA), where 1 mL of sterile medium was added to a cuvette to blank the spectrophotometer, and then 1 mL of the collected outflow was placed in a cuvette and measured. The pump was kept at the max flow rate for approximately 3–4 h after batch phase to remove planktonic cells and the additional HCO3- added during batch phase. Directly after flushing the bioreactor, water chemistry measurements were taken as they were above, and then the flow rate was dropped to 10 mL/min. Henceforth, the flow rate was only adjusted to keep the bulk fluid at approximately the pH and alkalinity of the starting conditions (max flow rate of 21 mL/min during week 3). To ensure consistency of the bulk fluid chemistry, pH and alkalinity measurements were taken twice daily (8–12 h intervals), just before changing carboys. Carboys were changed about every 8–12 h, depending on the current flow rate. To change carboys, the following steps were taken: (1) The pump was briefly turned off, and sterile aluminum foil was wrapped around the outflow tubing. (2) The bioreactor and currently connected carboy were moved to the biological safety bench on a roll cart. (3) Tubing that connected the carboy to the bioreactor was disconnected, and the male-port of the tubing still connected to the bioreactor was submerged in sterile 70% EtOH. (4) A new 10 L carboy containing the final medium was removed from the 10 °C refrigerator. (5) Aluminum foil covering the female-port of the tubing on the carboy was removed, and sprayed with sterile 70% EtOH. (6) Tubing from the bioreactor and carboy were then connected and placed on the roll cart, and moved back to the 10 °C refrigerator. (7) The pump was turned back on and flow resumed. One coupon holder rod was harvested every four days for 21 days, where two coupons were incinerated and weighed for mass loss and one coupon was fixed and stained for cell counting as described above. Surface area of aragonite coupons were estimated using the diameter of the coupon, the density of aragonite (2.93 g/cm3), and the mass of the coupon before and after bioreactor experiments. Density and mass were used to estimate the volume of the coupon, followed by deriving an estimated surface area. Using the mass of coupons before and after experiments, we calculated the total number of moles lost per unit area. Averaging these data out over time we were able to determine a dissolution rate in units of μmol CaCO3 • cm−2 • hr−1, which we then converted to μmol CaCO3 • cm−2 • yr−1 (8,760 h/year).
Confocal microscopy
We utilized the flow cell in conjunction with the ratiometric dye C-SNAFL-1 [5-[6]-carboxyseminaphthofluorescein] and Hoechst 33342 for measuring the in vivo pH of 21-day-old biofilms, and visualizing the biofilm, respectively. Coupons transferred to the flow cell were connected to a peristaltic pump at 3 mL/min until imaging occurred.
Spectrofluorometric assays
To calibrate C-SNAFL-1 photon excitations and to evaluate the dye’s potential use in measuring pH in biofilms, 1 mL aliquots of the final media with 10 mM HEPES buffer were adjusted to pH 5.0, 5.2, 5.6, 6.0, 6.4, 6.8, 7.2, 7.6, and 8.0. The final probe concentration was ~1.09 μM of C-SNAFL-1. Additional calibrations were performed with the addition of 4% (vol/vol) C. baekdonensis strain LH4.

$${mathrm{Ratio}} = left( {{mathrm{Ex}}_{488} – {mathrm{Ex}}_{488{mathrm{,bkgd}}}} right)/left( {{mathrm{Ex}}_{561} – {mathrm{Ex}}_{561{mathrm{,bkgd}}}} right)$$
(IV)

Confocal scanning laser microscopy
All biofilm images, pH and ratio calibrations in the flow cell were collected using a Nikon TiE inverted microscope equipped with an A1Rsi confocal scan head (Nikon). Hoechst 33342 fluorescence was excited with a 405 nm laser and collected between 425 and 475 nm using a regular PMT to visualize the biofilm. SNAFL emission was collected between 570 and 620 nm with a GaAsP PMT alternating excitation with the 488 and 561 nm lasers to obtain a ratiometric image. This image was used to calculate the pH across the field (below). 10X/0.45 PlanApo and 20X/0.75 PlanApo VC objectives were used to collect 512- by 512-bit resolution z-stacks. Depths in this paper are the distance from the substratum (base of biofilm) to the focal plane (distance furthest from the biofilm).
To visualize the biofilms, flow was suspended and coupons were stained with Hoechst 33342 (2 drops/mL medium) for 15 min and then flushed. C-SNAFL-1 was added to the medium at a final concentration of 20 μM. Flow resumed with C-SNAFL-1 in the medium and then was suspended for no more than 15 min to minimize the accumulation of acidic metabolites, which could artificially modify the local pH. Image planes were set up to visualize the coupon-attached biofilm and the bulk media adjacent to the coupon in the same image. Biofilms were excited at 405 nm, and emission was detected between 425 and 475 nm. pH microenvironment images were captured using dual excitation at 488 nm and 561 nm, and single emission at 600 nm. To determine the z-stack image parameters, we first excited the sample at 405 nm to visualize the base of the biofilm, and then switched to the dual excitation channels to determine the end of the focal plane (e.g. the coverslip; point furthest away from the biofilm in the z-dimension where the signal diminishes). After determining the z-distance to image, we imaged the biofilm at 405 nm, and then the pH of the microenvironment at 488/561 nm. The pH of the microenvironment was determined by calculating the ratio of excitation intensities (i.e., pixel values) between the two channels (Ex488 nm/ Ex561 nm). All intensities were determined using NIS Elements software.
Calibration standards for CSLM were performed in the flow cell with a sterile aragonite coupon and media with 20 μM C-SNAFL-1 and 10 mM HEPES buffer at pH 8.0, 7.8, 7.7, 7.55, 7.2, 7.0, and 6.8. Calibrations were performed with identical microscope settings as in the experiment above. Titration curves of pH versus ratios were calculated using NIS Elements software according to Eq. IV and were used to convert C-SNAFL-1 excitation ratios to pH values.
Image analysis
pH gradients within the biofilm and surrounding microenvironment were evaluated with NIS Elements software by using kymographs on the ratio images (Ex488 nm/ Ex561 nm). Each kymograph was ~500 μm in length (xy) and ~100 μm in depth (z). The resulting kymograph image had 4–5 regions of interest selected, including 2–3 spanning the biofilm into the bulk fluid directly above, and 2-3 in the bulk fluid adjacent to the biofilm. Regions of interest varied in z-depth depending on the thickness of the biofilm, though all were 13 μm in width (xy).
Estimation of CaCO3 dissolved from various seep locations
Seep locations
PDC1 – Dive, SO 162; Site, Point Dume; Feature, Chimney complex 1; Latitude, 33 56.45753693; Longitude, 118 50.70879299; Depth (m), 729.522
PDC2 – Dive, SO 164; Site, Point Dume; Feature, Chimney complex 2; Latitude, 33 56.50074983; Longitude, 118 50.64165898; Depth (m), 725.723
PDC3 – Dive, SO 164; Site, Point Dume; Feature, Chimney complex 3; Latitude, 33 56.45417243; Longitude, 118 50.70728425; Depth (m), 728.966
Determining total surface area
Each image taken by submersible at Point Dume had two scale bars generated at the time of imaging, one in the background and one in the foreground, with each representing 10 cm. These two scale bars, in conjunction with a designed macro in Fiji/ImageJ were used to interpolate the total surface area within each image. The macro is based on the equation y-y1 = m(x-x1). The slope, m, was determined using the equation ((y2-y1)/(x2-x1)), where y2 = 10 cm/(length in pixels of the foreground scale bar), y1 = 10 cm/(length in pixels of the distant scale bar), x2 = (y-coordinate of the foreground scale bar), x1 = (y-coordinate of the distant scale bar). Each image was imported into Fiji/ImageJ, and scaled to 1333 × 750 pixels. All scales were removed to put all measurements in pixel values. Centroid and integrated density were added to set measurements. Each image was duplicated and the duplicated images were changed to 32-bit images. The macro was then run on each respective image. A polygon was then drawn around the visible area within each image, and added to the ROI manager. The ROIs were added to their respective duplicate and measured. These data gave us the total visible surface area present in each image.
Determining surface area of exposed carbonates and carbonated-attached bacterial mats
Polygons were drawn around all exposed carbonates within the visible region. These ROIs were then added to their respective duplicated image, and the total area of exposed carbonates was measured. Carbonate ROIs that contained small regions in which bacterial mats were not visible had “negative” ROIs drawn around them. When applicable, these negative ROIs were subtracted from the total exposed carbonate measurement, resulting in the total mat-on-rock surface area measurement. It is likely that bacteria are also colonizing the surfaces on which no obvious mat can be observed, but removal of these areas from consideration was done to provide a conservative lower estimate of bacterial coverage.
Moles of carbonate rock dissolved was calculated as follows
Experimentally-derived carbonate dissolution rate = 1773.97 μmol CaCO3 • cm−2 • yr−1.
Apply dissolution rate to seep surface area covered by rock (e.g. Hydrate Ridge) to obtain the annual dissolution; 17.7397 mol CaCO3 • m−2 • yr−1 * 3.00E + 05 m2 = 5.32E + 06 mol CaCO3 • yr−1.
Apply 92.77% average carbonate-attached bacterial mat percent coverage (Supplementary Table 3) to annual dissolution; (5.32E + 06 mol CaCO3 • yr−1) • (0.9277) = 4.94E + 06 mol CaCO3 • yr−1.
We then applied these calculations to the remaining five seep sites in Table 1 to obtain the annual amount of carbonate dissolved per seep.
Table 1 Estimation of moles of carbon potentially released to the ocean/atmosphere system from the weathering of carbonate rocks via lithotrophic sulfur-oxidation at seep sites where carbonate coverage has been estimated.
Full size table More

We used high-throughput qPCR to screen for 58 infective agents in four Atlantic salmon farm cohorts from British Columbia throughout their production cycles. We measured presence and copy number for target genetic sequences, characteristic of specific viral, bacterial, and eukaryotic agents, including several recently discovered viruses30,31, known or suspected to cause disease in salmon. These agents displayed various temporal patterns of prevalence and intensity, with several displaying elevated levels in dead and dying fish.
The data and analyses we have presented provide a unique look into the epidemiology of farmed salmon populations, and wildlife/livestock diseases generally. No past studies have had access to multiple farmed-salmon cohorts, throughout their production cycles, with the capacity to molecularly screen for a large suite of infectious agents. Other work has reported agent data for dead-sampled fish collected in BC as part of Fisheries and Oceans Canada’s farm audit program40, but such analyses lack the time-series nature of the results we have presented. To our knowledge, no other studies have provided such detailed, comprehensive information for infective agents in domestic or wild populations over time. This study, therefore, presents a substantial step toward effectively monitoring shared wildlife/livestock diseases, made possible by cutting-edge technology, as predicted previously22.
While our findings offer specific insight to salmon farmers, aquaculture managers, and those concerned with the disease ecology of sympatric wild salmon, we caution that results remain correlative, and relevant patterns (e.g. apparent mortality signatures) require further investigation. Unfortunately, a lack of regular testing of dead and dying fish (collection was opportunistic, at farms’ discretion) resulted in potential for associated patterns to be obscured. Due to this potential bias in the sampling design, we are unable to draw conclusions related to farm-level mortality rates, but several agents showed patterns of note, including elevated levels in dead and dying fish.
Agent patterns
Perhaps the clearest single-agent pattern—the elevated load of T. maritimum in dead and dying fish (Fig. 3B)—matches generally accepted patterns in aquaculture. Induced tenacibaculosis can be responsible for substantial on-farm mortality worldwide41, and mouthrot resulting from T. maritimum in the east Pacific causes substantial losses42. In our study, mouthrot was noted during veterinarians’ sample processing for cohorts one, three, and four in the months after ocean entry. We note that elevated levels in dead and dying fish could represent the bacterium’s acknowledged role as an opportunistic pathogen41, rather than a direct cause of mortality. We also note the positive correlations between T. maritimum load and that of a number of different agents (Fig. 5), consistent with the view that T. maritimum may facilitate co-infections43. Given its high overall prevalence in fish (Table 3), secondary factors—such as co-infections—might exacerbate infection with T. maritimum, playing a role in mortality.
K. thyrsites intensity was elevated in dead and dying fish for cohorts three and four, around the time they were transferred to their final marine locations (Fig. S8). In both cases, the cohorts finished their production cycles in farms on the central east coast of Vancouver Island (Fig. 1), a region in which the risk of K. thyrsites infection is acknowledged to be high44. This myxozoan parasite is economically important due to post-mortem myoliquefaction seen in infected fish, but it is not considered a pathogen45, and it is unclear why higher gene-copy levels would be observed in dead/dying fish. K. thyrsites may merely replicate faster in stressed fish (in this case due to transport). Our surveillance of pathogens did not include skeletal muscle tissue, where K. thyrsites spores develop, but it did include heart, which can be infected by the parasite46. We note that K. thyrsites was correlated with PRV, with both agents known to infect muscle tissue (although red blood cells are the primary infective tissue for PRV). Follow-up histopathological investigation may provide some insight into K. thyrsites distribution and any associations with pathology or patterns of co-infection.
PRV, which is the causative agent of Heart and Skeletal Muscle Inflammation (HSMI)47 and has recently generated controversy in BC28,29,48, shows several patterns of note. PRV prevalence increased to near ubiquity over time (Fig. 2D), concurrent with an increase, peak, slight decline, and then stabilisation of intensity (Fig. 3B). Although our perspective is limited to sampled fish, with a noted potential for bias, the observed PRV trends were consistent across all four cohorts, and the intensity patterns are consistent with previously reported dissemination, peak replication, and long-term persistence phases of the virus within hosts29,48. Past findings suggest that PRV may induce an antiviral response in hosts that can protect them against certain co-infections49,50. Perhaps counter to the generality of this claim, PRV and ASCV exhibited the strongest load correlation out of any we observed across our data set (Fig. 4). ASCV was originally isolated from salmon with HSMI, and was initially thought to play a role in the disease51. Other work has found no relationship between ASCV and PRV infections or HSMI52. In the case of a related baitfish calicivirus, however, there is evidence that viral co-infection is linked to disease manifestation53, so further work is needed to tease these relationships apart. In general, dead and dying Atlantic salmon in our study did not show elevated prevalence or intensity of PRV, except shortly after ocean entry in cohort one (Figs. 2D, 3B). This mortality signature corresponds to the onset of lesions diagnostic of HSMI in cohort one, which subsequently spread to affect the majority of that farm population for most of a year29.
The gill chlamydia bacterium, C. Syngnamydia salmonis, showed a consistent trend towards elevated prevalence in dead and dying salmon (Fig. 2B). Observed intensity was low, however, often averaging approximately a single copy (Fig. S15). Sequencing has validated past detections of this agent on the Fluidigm BioMark™, and has also revealed SNP diversity within the primer-binding region, resulting in potential underdetection (Miller et al. unpublished). Given a putative mortality signature and the lack of prior epidemiological study of this recently discovered agent54,55, we would suggest further work on C. Syngnamydia salmonis.
Ephemeral mortality signatures appeared for several agents. F. psychrophilum was clearly elevated in dead and dying fish in-hatchery, although we only had access to two hatchery cohorts and cannot draw general conclusions. Intensity of both the ASCV and CTV-2 were elevated in sampled dead and dying fish from at least one cohort shortly after ocean entry (Figs. 3A, S2). Both viruses were also present in-hatchery. The previously reported Cutthroat trout virus appears to be apathogenic56 in trout, and has been detected in Atlantic salmon57. Little is known about the novel variant for which we screened, although in situ hybridisation has revealed that infection can be systemic and extensive in the brain (Mordecai et al. 2020). As for the ASCV, associated pathology was found to be likely due to PRV contamination51. Extremely limited information about these two viruses, paired with our findings, warrants further investigation (e.g. with histopathology and in situ hybridisation) to determine if either virus is linked with pathology. As both these viruses were detected in Chinook salmon (Mordecai et al. 2020), and considering their high prevalence in Atlantic salmon farms, the potential risk they pose to wild Pacific salmon populations should be a priority for future research.
Infectious agent levels overall, as measured by relative infectious burden, showed a clear trend in smolts coming out of freshwater hatcheries for cohorts three and four. There, infectious burden was much higher (in one case 10 000 times higher) in dead and dying fish than in live-sampled fish. While the effect dissipated once fish entered the marine environment, it is clear that hatchery fish are dying with—or of—elevated levels of infection. The patterns we observed likely reflect the transition from freshwater to saltwater, with a coincident shift in infective-agent communities58. Smoltification has also been associated with immune depression59, and elevated infectious burden around the time of ocean entry may reflect this. Where we had dead/dying hatchery samples, however, infectious burden was elevated weeks before ocean entry, hinting at the potential for problems in-hatchery.
Across all agents, we observed apparent coinfection signals that clearly differed from random chance (Figs. 5, S21). We point out, however, that due to the longitudinal nature of our study and cursory investigation of agent correlations, the correlation results almost certainly indicate shared temporal trends that may or may not indicate underlying interactions. For example, Candidatus Branchiomonas cysticola and Flavobacterium psychrophilum were positively correlated with each other, but negatively correlated with a number of other agents. This could be due to both agents being common in-hatchery but not in the marine environment (Figs. S3, S6), counter to many other agents’ patterns. Alternatively, the correlation could be due to a biological relationship, perhaps in relation to gill health. We intend to follow up on a number of correlational patterns.
Agent idiosyncrasies
Several agents showed unexpected patterns, or patterns that may be connected to their particular biology.
The putative Narna-like virus, a recently discovered agent31, showed elevated prevalence in dead and dying fish (Fig. S9). This pattern was mainly due to over-representation in dead-sampled fish, as we detected the agent in 13.2% of dead fish, 1.6% of moribund fish, and 0.4% of live-sampled fish in saltwater. Given that Narnaviridae, of which the putative Narna-like virus is a member, is thought mainly to infect fungi60, this virus may be associated with a fungal decomposer. This is speculative, however, and recent genomic evidence from across taxa suggests that the Narnaviridae may be much more widespread than previously thought61.
Counter to the common trend, P. pseudobranchicola tended to be less common in dead and dying fish than in live-sampled fish (Fig. 2C), with dead fish, in particular, tending to exhibit the lowest levels (results not shown). P. pseudobranchicola primarily infects the pseudobranch62, a structure near the gills involved in oxygenating blood in the eye. Infection also occurs in tissue collected for this study, especially gill63, and we speculate that loads in dead fish could be reduced due to myxospore release or degradation of delicate gill tissue after host death. Given that we did not sample the pseudobranch, it is likely that our data underestimates the load of this organism.
The sampling environments (freshwater or marine) of several detections were unexpected. In particular, we detected K. thyrsites and T. maritimum (Fig. 3C) in freshwater hatcheries, although these agents are considered marine species64,65. It is possible that these hatcheries introduced saltwater in the weeks before ocean transfer, to prepare smolts for release. We also detected F. psychrophilum, considered a freshwater bacterium66, in marine net pens (Fig. 2A). The bacterium is known to survive in brackish water67, however, and this is not the first time it has been detected in a marine setting40,68.
Broader connections
Not all infective agents cause disease, and even agents that do can be present long before—or long after—clinical symptoms. Our work presents only a piece of the puzzle in what is a multifaceted, complex scenario of shared wildlife/livestock disease in salmon aquaculture. The controversy surrounding PRV in BC, as an example, illustrates this complexity. While conventional lab challenges using PRV from BC sources have failed to reproduce in BC fish the extent of HSMI lesions observed on Norwegian farms48,69, work related to our study has been able to identify and shed light on HSMI, and related jaundice/anemia in Chinook salmon, in BC salmon farms28,29. While we saw a putative mortality signature in one cohort during this study, the normal course of PRV infection was not always associated with mortality (e.g. Figs. 2D, 3B). More work will be required to elucidate the nuances of PRV infection, factors that induce associated disease, and possible resultant mortality. A fruitful place to start would be to carry out sampling and diagnostics of dead and dying fish in farms and pens experiencing elevated mortality.
Although we have shown putative mortality signatures for several infective agents in farmed Atlantic salmon, these are not necessarily the agents that pose the greatest risk to wild salmon. For one thing, a given agent need not produce the same effects in different species28,70. For another, contact between populations may not coincide with infection maxima. Depending on when farm smolts enter the marine environment, for example, PRV could be at low prevalence in the spring, when a number of wild Pacific salmon species migrate as juveniles15. Other times of year would be more relevant for interactions with other wild species, and there is much scope for transfer between farmed and wild environments. In addressing shared wildlife/livestock disease, we need to consider both wildlife and livestock as populations that serve as potential reservoirs of disease agents, and are susceptible to outbreaks71. In this context, surveillance and monitoring are essential facets of disease management23, providing raw material to develop understanding of disease and build effective management strategies. Parallel work is monitoring wild populations for the same agents we have investigated here, with the prospect of cross-referencing patterns and impacts72,73,74.
The ubiquity of infectious agents on the farms leads naturally to discussion of potential control strategies, which present a variety of challenges in aquaculture. Vaccination has proven successful at times, but the salmon aquaculture industry has a somewhat chequered history with uptake, since vaccination can affect host growth, and thus the bottom line13. In addition, vaccines have only been developed for a handful of agents. Reducing translocations can be an effective control strategy on land20,78, but transmissive properties of the marine environment and highly mobile marine carrier hosts pose challenges to isolating host populations geographically (Krkosek 2015). Our findings provide circumstantial evidence that some agents (e.g. K. thyrsites) respond to translocations. The fact that two of our four focal cohorts moved substantial distances throughout their respective marine production may be cause for concern, considering the infective-agent populations we have shown those cohorts to have harboured. In general, aquaculture-associated disease and related management decisions have a history of generating political controversy75. Infective-agent monitoring and analyses are critical for designing, implementing, and evaluating effective disease-control measures, and for bridging divides in debate surrounding aquaculture.
With respect to the aquaculture industry, the tools we employed in this study may prove useful for disease management and fish health. We have shown that for many agents, patterns of infection in dead and dying fish mirror those in live fish. By integrating high-throughput infectious-agent screening with existing monitoring of dead fish, farm vets and managers could access a wealth of otherwise unavailable or costly information. Combining such results with strategic sacrificial sampling of live fish during mortality peaks could allow additional insight into which agents may be driving mortality. Protecting the ‘herd’ (and its wild neighbours) may justify such mortal sampling. Furthermore, in other ongoing work, we have seen that much infective-agent information is accessible via nonlethal gill biopsy, which also enables high-throughput screening for gene expression patterns associated with various patterns of stress76 and disease77. Used appropriately, such a combination of tools could be very powerful.
Disease monitoring is never complete, and detection always lags behind pathogen spread78, but new technologies—such as those we employed here – can facilitate efficient, lower-cost surveillance and monitoring. Surveillance for existing pathogens and identification of previously unknown pathogens is part of the integrative approach required to understand and control existing and emerging infectious diseases22. Here, we have further demonstrated the utility of high-throughput, modern genetic techniques for monitoring known infective agents and for generating information about previously under-studied agents26,29,30,31,37,38. Further work will target the risk of transfer between wild and farmed hosts and prioritize threats to salmon, farmed and wild. More