Ecology

General patterns of dietary quality and compositionAveraged across the months, site-level [CP] ranged from 62.6 to 147.3 mg g−1 and averaged 96.7 ± 3.3 mg g−1. [DOM] ranged from 571.3 to 643.6 mg g−1 and averaged 605.8 ± 2.4 mg g−1 while [DOM]: [CP] ranged from 4.4 to 10.3 with an average of 6.9 ± 0.2.Examining seasonal patterns averaged across all sites, [CP] averaged 82.5 ± 5.4 mg g−1 in April, peaked at 113.2 ± 3.3 mg g−1 in June, and declined to 80.6 ± 4.5 mg g−1 in September (Supplementary Table S1, Fig. 2). Similarly, [DOM] averaged 583.0 ± 6.1 mg g−1 in April, peaked at 630.2 ± 3.4 mg g−1 in June, and declined to 585.1 ± 4.2 mg g−1 by September (Supplementary Table S1, Fig. 2). [DOM]: [CP] averaged 8.1 ± 0.5 in April, declined to 5.8 ± 0.2 in June, and increased to 8.1 ± 0.4 by September (Supplementary Table S1, Fig. 2).Figure 22019 monthly dietary quality metrics averaged (± S.E.) across all sites. Shown are (a) crude protein concentrations ([CP]), (b) digestible organic matter concentrations ([DOM]), and (c) the ratio between [DOM] and [CP].Full size imageExamining dietary functional group composition across sites, on average 38.2 ± 2.6% of dietary protein intake came from grasses, ranging from 12.7 to 82.2% across sites. Examining patterns for the two grass functional groups, 26.5 ± 2.9% (1.3–82.0%) of protein intake was derived from cool-season (C3) graminoids and 11.7 ± 1.6% (0–36.7%) came from warm-season (C4) grasses. Of the Eudicots, protein intake from legumes averaged 37.8 ± 2.8% (1.3–70.2%), from non-leguminous forbs averaged 21.5 ± 2.0% (0.2–68.0%), and 2.5 ± 0.8% (0–31.6%) from woody species.Examining monthly patterns across sites, C3 grass protein intake was highest in May (40.8 ± 5.0%) and lowest in September (15.8 ± 2.8%) while C4 grass protein intake peaked in September (16.2 ± 2.9%) and was lowest in July (10.2 ± 2.4%) (Supplementary Table S2, Fig. 3). Legume protein intake was highest in August at 55.8 ± 5.3% and was lowest in May (20.0 ± 4.0%) (Supplementary Table S2, Fig. 3). Forb protein intake peaked in June at 27.6 ± 3.5% and was lowest in August (14.2 ± 2.8%) (Supplementary Table S2, Fig. 3).Figure 3Average contributions of different functional groups to 2019 dietary protein intake for bison across sites for each month from April to September. Data based on the 200 most abundant Exact Sequence Variants (ESVs), which were assigned a consensus taxonomy and then a function classification. Cool-season graminoids includes C3 grasses, sedges, and Equisetum. Warm-season grass was derived exclusively from grasses. Legumes only included species from Fabaceae.Full size imageClimate and dietary qualityExamining patterns of [CP] across sites, [CP] was highest in cool, wet climates (Fig. 4, Supplementary Table S3). For example, bison in June at a site with 1200 mm of rain and 6 °C MAT would have [CP] of 186.3 mg g−1. Bison in June at a site with 400 mm of rain and 18 °C MAT would have [CP] of just 67.8 mg g−1. There was also a statistical interaction between the identity of the month and MAT on [CP] (Fig. 4, Supplementary Table S3). In April, [CP] tended to increase with increasing MAT (2.51 ± 1.46 mg g−1 °C−1), as warm-climate sites tended to have higher [CP] than cold-climate sites (Fig. 4, Supplementary Table S3). By May, colder sites tended to have higher [CP] (− 2.01 ± 1.81 mg g−1 °C−1), which became stronger by June (− 5.34 ± 1.83 mg g−1 °C−1) and lasted through September (Fig. 4, Supplementary Table S3). Examining patterns of functional group composition on top of climate relationships, [CP] was lower when greater amounts of warm-season grasses were in the diet, independent of climate and season (Supplementary Table S4).Figure 4Maps of dietary crude protein and digestible organic matter concentrations for non-forested areas. Maps are modeled based on Supplementary Table S3. Units are mg g−1. Maps generated utilizing the raster package for reclassifying and masking pixels in R 3.5.2.Full size imageLike [CP], [DOM] was also highest in cool, wet climates (Fig. 4, Supplementary Table S3). For example, bison at a site with 1200 mm of rain and 6 °C MAT in June would have [CP] of 670.4 mg g−1 as opposed to bison at a site with 400 mm of rain and 18 °C MAT in June, which would have [CP] of just 580.4 mg g−1. In April, bison in warm-climate sites tended to consume a diet that was higher in [DOM] than bison in colder-climate sites (2.04 ± 1.73 mg g−1 °C−1) (Fig. 4, Supplementary Table S3). By May, [DOM] was invariant across MAT gradients (− 0.08 ± 1.98 mg g−1 °C−1) and by June, cold-climate sites had higher [DOM] (5.51 ± 2.00 mg g−1 °C−1) (Fig. 4, Supplementary Table S3).Examining patterns of DOM:CP reveals the seasonal and spatial patterns of protein limitation. In general, bison in cool, wet climates had the lowest [DOM]: [CP] (Supplementary Table S3). Yet, the statistical interaction between MAT and month shows that in April, bison in cold climates were more protein limited than in warm climates (− 0.16 ± 0.11 °C−1) (Supplementary Table S3). By May, [DOM]: [CP] tended to become higher in warm climates, with the gradient becoming strongest by August (0.61 ± 0.15 °C−1) (Supplementary Table S3).Climate and dietary compositionExamining cross-site relationships between the relative amounts of different functional groups in diet and the predictors of climate and season, the proportion of cool-season grass in the diet was highest in cool sites (decreasing at a rate of 3.8 ± 0.5% °C−1), with no significant influence of MAP (P = 0.16) (Fig. 5, Supplementary Table S5). The proportion of cool-season grass in the diet varied among months, but there was no difference among months in the relationship with MAT (P = 0.54). In contrast, the proportion of warm-season grass in diet was greatest in hot, dry regions (Fig. 5, Supplementary Table S5). For example, bison at a site with 400 mm of rain and 18 °C MAT would have 42.9% of their dietary protein from warm-season grasses as opposed to a site with 1200 mm of rain and 6 °C MAT, where warm-season grasses would provide 0% of their dietary protein.Figure 5Maps of functional group composition of bison diet (excluding woody plants, which had low abundance and little pattern with climate) for non-forested areas. As there were no interactions between either MAT or MAP and the identity of the month, only maps for June are shown here. See Fig. 2 for how functional group composition of the diet changes over the six six months. Maps generated utilizing the raster package for reclassifying and masking pixels in R 3.5.2.Full size imageAmong Eudicots, there was no seasonal variation in the percentage of forbs in the diet, but the percentage of dietary protein from forbs was highest in hot, dry regions (Fig. 5, Supplementary Table S5). For example, bison at a site with 400 mm of rain and 18 °C MAT in June would have 54.2% of their dietary protein from forbs as opposed to a site with 1200 mm of rain and 6 °C MAT in June, where it would be just 9.6%. Legumes had strong seasonal variation in its dietary contribution, but little variation with climate, increasing at a rate of 2.0 ± 0.8% per 100 mm MAP (P = 0.02) (Fig. 5, Supplementary Table S5).Comparison with 2018 dataComparing [CP] between years, June [CP] was higher on average in 2019 than 2018 (113.2 ± 3.3 vs. 93.6 ± 3.2 mg g−1; P  0.3 for both MAT and MAP) (Table 3). There were also no significant relationships with climate for forb or woody contribution to diet between years (Table 3). Despite many of the similarities between years, comparing dietary functional group composition in September between years, legumes contributed 20% more protein and warm-season grasses 14% less in 2019 than 2018.Table 3 Model results for comparing functional group abundance in bison diet for June and September between 2018 and 2019.Full size table More

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This work was undertaken at the University of California Davis Dairy Teaching and Research Facility from June to September 2018. All experimental protocols were approved by and carried out in accordance with the University of California Davis Institutional Animal Care and Use Committee (protocol # 20505).TreatmentsWe enrolled all female calves born between June 19 and September 1 2018, for a total of 28 Holsteins and 8 Jerseys. Our sample size was determined by the availability of calves being born in our herd of approximately 105 lactating cows during this period. Calves were blocked by birth order and randomly allocated to 1 of 3 treatments balanced for breed: disbudded the morning of (Day 0) or 21 days before (Day 21) the startle test, or sham-disbudded (Sham, n = 12/treatment). Among the control calves, half were sham-disbudded the morning of the test, whereas the other half underwent the procedure 21 days earlier. Birth weights were similar across treatments (mean ± SD; Day 0: 35 ± 5 kg; Day 21: 35 ± 6 kg; Sham: 36 ± 9 kg). The startle test occurred between 25 and 32 days of age for all calves. Thus, all Day 0 calves and half of the Sham calves were disbudded between 25 and 32 days of age, and all Day 21 calves and half of the Sham calves were disbudded between 4 and 11 days of age. This design meant all animals were at the same stage of cognitive and motor development during data collection. This was a priority for us because we expected age to strongly influence behavioural responses during the startle test. While it is also possible that disbudding at different ages may affect responses, previous research suggests disbudding has similar outcomes across this range13,15,20.Animal husbandry and housingImmediately after birth, calves were housed individually in outdoor enclosures consisting of a plastic hutch (2.0 m long × 1.5 m wide) and a wire-fenced pen (2.0 m long × 1.5 wide × 0.9 m high). The enclosures were spaced 0.5 m apart and bedded with sand approximately 15 to 20 cm deep.Calves were bottle-fed colostrum twice a day for 5 days. From 5 days of age, calves received milk replacer (26% CP and 16% fat, 15% total solids; Calva Products Inc., Acampo, CA) in bottles at 0645, 1245, and 1845 h. At each meal, Holsteins were fed 1.9 L from 1 to 13 days, 2.4 L from 14 to 23 days, and 2.8 L from 24 days. Jerseys received 1.4 L from 1 to 13 days, 1.9 L from 14 to 23 days, and 2.4 L from 24 days. Water and starter (18.3% CP, 2.8% fat, 4% crude fat; Associated Feed & Supply Co., Turlock, CA) were provided ad libitum in buckets. As part of a separate concurrent study, 11 calves (3 Sham, 3 Day 21, 5 Day 0) received chopped mountain grass hay (34% CP) ad libitum.DisbuddingDisbudding occurred between 730 and 1000 h. For the procedure, the calf was restrained in a head device in her home enclosure21. A 5 × 5 cm patch of hair was clipped with a size 40 electric razor blade on each side of the head to locate the horn bud. We used a 20 gauge × 25 mm needle to administer a cornual nerve block consisting of 5.5 mL buffered lidocaine (2% lidocaine hydrochloride diluted with 8.4% sodium bicarbonate in a 10:1 ratio). If the horn bud was not numb after 10 min, as assessed by pinprick, we gave an additional 2 mL of buffered lidocaine (13% of horn buds). An electric cautery iron (X50, Rhinehart Development Corp., Spencerville, IN) was fitted with a 1.3 cm tip and heated to 439 ± 15 °C (mean ± SD). It was applied to the horn bud for 17 ± 5 s (mean ± SD). Immediately before disbudding, the calf received approximately 1 mg/kg of meloxicam tablets in a gelatin capsule (3.5 g; Torpac Inc., Fairfield, NJ). For Day 0 calves, meloxicam was given after the startle test had occurred later that same day (maximum 12 h later) to ensure the calf was in a drug-free state during the test. Sham-disbudded calves received the same treatment, with the exception that the iron was ambient temperature and the gelatin capsule was empty. Sham calves did not receive meloxicam because the Animal Medicinal Drug Use Clarification Act limits nontherapeutic off-label use of this drug22. SJJA performed all disbudding procedures.ArenaWe tested calves individually in a single 10-min period in a shaded outdoor arena bedded with 10 to 15 cm of sand. The arena was divided into a waiting pen (2.0 × 1.5 m) and a test pen (3.0 × 5.5 m) constructed of 0.9 m high wire panels (MidWest Homes for Pets Foldable Metal Exercise pen, Muncie, IN). A rolling gate provided access between the pens (Fig. 1).Figure 1Aerial view of the arena used for startle tests, including the position of the milk bottle and speaker used to broadcast the startle noise. Figure is drawn to scale.Full size imageA bottle containing 500 mL of the calves’ regular milk replacer was secured to the panel opposite the entrance to the test pen. The bottle was fitted with a rubber teat positioned 80 cm above the ground. Between calves, a fresh bottle was placed in the arena and urine and feces were removed with a shovel.Testing procedureCalves were habituated to the arena for 15 min daily between 700 and 1100 h for 3 consecutive days before the startle test. Calves were brought to the arena in the same order each day, with order balanced across treatments. During habituation, no startle stimulus was delivered, but otherwise the same procedure followed on test days was applied.The startle test occurred between 1530 and 1800 h (Supplementary Video S1). The calf was transported from her home pen to the waiting pen in a cart (Caf-Cart, Raytec, Ephrata, PA). The test began when the gate providing access to the test pen was opened and ended after 10 min. The gate was closed behind the calf after she had entered so that the waiting pen was inaccessible during the test. Three observers were seated quietly 3.5 m away from the pen during the test, and were partially concealed behind a tree branch. One observer remotely controlled the speaker broadcasting the startle noise, while the other two observers were present to respond if a calf escaped from the arena (only one calf jumped out, on the first day of habituation, and was promptly escorted back into the pen). Calves showed no apparent responses to the observers and had no visual contact with other animals.As soon as the calf’s mouth was within a tongue’s reach of the teat, a 0.4 s, 105 ± 2 dB burst of white-noise was emitted through a wireless speaker (OT4200 Big Turtle Shell, Outdoor Tech, Laguna Hills, CA) mounted directly behind the bottle. The noise was created using an online signal generator23. We measured the sound level using a decibel meter (BAFX Products, Milwaukee, WI) held 30 cm in front of the bottle, approximating the distance of the calf’s ears to the source.Behavioural data collectionTests were recorded with a camcorder (HC-V180, Panasonic, Kadoma, Japan) positioned on a tripod approximately 3 m away from of the pen. One trained observer, blinded to the treatments, scored behaviours in all videos taken of the startle test and the third day of habituation (Table 1). Videos were analysed using BORIS (Behavioural Observation Research Interactive Software24). Intra-observer reliability was calculated using 12 randomly selected videos of the startle test (Intraclass correlation coeffcient ≥ 0.95).Table 1 Behavioural definitions used to evaluate calves’ responses in an arena test.Full size tableAccelerometers (Hobo Pendant G Acceleration Data Logger, Onset Computer Corporation, Bourne, MA) were used to assess the magnitude of the startle response. On habituation and test days, we fitted calves with a triaxial accelerometer set to record acceleration in the x-, y-, and z-axis every 0.05 s. The accelerometer was placed in a pouch, strapped around the right hind leg, and secured with Vet Wrap (Co-Flex, Andover Coated Products Inc., Salisbury, MA) while the calf was in the waiting pen of the arena, immediately before the gate to the test pen was opened. Data were downloaded using HOBOware Pro Software (Onset Computer Corporation, Bourne, MA). To calculate the magnitude of the startle response, we summed total acceleration in all 3 axes over the startle duration for that calf. Total acceleration was calculated as the square root of the sum of squared acceleration in each axis25. No startle response was recorded for one calf who did not approach the bottle on the test day.All calves were weighed the morning of the startle test (mean ± SD; Day 0: 56 ± 10 kg; Day 21: 55 ± 9 kg; Sham: 55 ± 11 kg).Wound healing and sensitivityWe measured sensitivity via mechanical nociceptive thresholds around the horn bud area 1 to 2 h after the startle test using a digital algometer fitted with a 4-mm-diameter round rubber tip (ProdPlus; TopCat Metrology Ltd., Little Downham, UK). The calf was restrained in the head device in her home pen and blindfolded to reduce responses to visual cues. We then applied an increasing amount of force to the edge of the disbudding wound, or intact horn bud for sham calves, as described previously13. The test ended when the calf moved her head or a maximum cut-off point of 10 N was reached. We repeated the test if a fly landed on the head, a loud noise occurred, or the calf urinated or defecated. If a test was interrupted 3 times, it was abandoned (0% of tests).Wound sensitivity was tested at the lateral and caudal edges of each wound or the equivalent location on sham calves. The order of test sites was: left lateral, left caudal, right caudal, and right lateral. To ensure force was applied at a consistent rate, personnel operating the algometer were trained and met a set of rigorous criteria before performing the tests13. We calculated the rate that force was applied in each test from video recordings (0.29 ± 0.10 N/s; 2% of videos missing). If force was increased at a rate  0.6 N/s or video was missing, the data were excluded (3% of tests). Due to the nature of the tests, the operator of the algometer was not blind to treatment.We took digital photographs of the wound with a DSLR camera (D5300; Nikon Corp., Tokyo, Japan) after sensitivity testing was completed. Photos were taken 15 cm from the wound. One person scored the photos for tissues present in the wound bed using a 0/1 scoring system13. Due to the clear differences in Day 0 and Day 21 wounds, the scorer was not blind to treatment.Statistical analysisDue to the presence of zeros in the data, we used zero-inflated beta regressions to assess the effect of treatment (Sham, Day 0, Day 21) on the proportion of time suckling on the third day of habituation and during the startle test. A zero-inflated beta regression is a mixture of two models: a beta model for estimating non-zero proportions and a logistic model for estimating the probability of zeroes26. This approach allowed us to infer treatment effects on both the occurrence and duration of suckling. General linear models were used to test the effect of treatment on the duration of the startle response and its magnitude as measured from the accelerometer data.We analyzed the effects of treatment on latency to approach the bottle and latency to return after startling using parametric survival regression models with a log-logistic distribution. Days on which the calf did not perform the behaviour within the allotted time (15 min for habituation, 10 min for startle test) were handled as right-censored data.We ran a general linear model to test the effect of treatment on wound sensitivity. A preliminary analysis indicated that there was no effect of side (left vs right) or location (caudal vs lateral) on wound sensitivity, so we averaged data for each calf into one score.Data were analysed in R, version 3.5.227. General linear models were fitted using the “lm” function in base R. We confirmed homogeneity of variance and normality using residuals vs fits plots and Q-Q plots, respectively. Beta and survival regressions were performed with the “glmmTMB” function in the glmmTMB package version 1.0.028, and the “survreg” function in the survival package version 2.3829, respectively. If treatment effects were identified in any of the models (P  More

This is the first study to evaluate the mercury content in the fur of Siberian tigers in the Far East of Russia. It is a commonly recognized fact that fish are the main source of mercury entering the organism of predators and the trophic network of the ecosystem. In some areas, the seasonal abundance of salmonids can provide tigers with protein: masu (Oncorhynchus masou) from April to early July, chum (O. keta) in late autumn and up to December and pink salmon (O. gorbuscha) in July–early October. However, our observations during 1976–2018 (15 (Poddubnaya, unpublished data)) and data on mercury content show that tigers do not eat salmon often. Tigers do not hunt the redfin dace (Tribolodon hakonensis) a cyprinid fish, moving up in huge swarms from April to June.Although tigers were never observed to consume fish in mass quantities, as is the case with bears, one would expect that the total concentration of mercury (THg) in the body of tigers from two sections of the Sikhote-Alin (basin drainage of the Sea of Japan and the catchment of the Amur River) would vary depending on the availability of anadromous fish. The rivers of the Sea of Japan basin are shorter and shallower than Amur’s tributaries. Therefore, the likelihood of a tiger catching fish here seems to be higher. However, Amur’s tributaries are richer in fish and the probability of catching fish has to be no less than that observed in the coast. Thus, it can be assumed that the proportion of fish in tiger’s diet on the coast and in the inland region should be similar. Apparently, the consumption of salmon by tigers can be neglected in this analysis.The minor role of fish in the Siberian tiger diet is further evidenced by comparing its average mercury content with other large felids known to consume fish and other aquatic animals. Thus, individuals of Florida panther (Puma concolor coryi), consuming aquatic and fish-eating animals have elevated levels of MMHg—1.62 ± 1.87 mg/kg or 1.84 mg/kg THg 20. The average mercury concentration in the jaguar (Panthera onca), mainly preying on fish and alligators, reaches even higher values of up to 4.27 mg/kg (from 2.13 to 7.26 mg/kg) 21. The average THg content in the Siberian tiger is 0.383 ± 0.062 mg/kg indicating that it eats little fish, if any.In the south of the Russian Far East, some ungulates caught by the tiger on the eastern macro slope of Sikhote-Alin periodically go to the sea to lick salt and eat algae. This can lead to some increase in the level of mercury in their tissues and in the tiger along the trophic chain. In addition, ungulates, especially deer, can eat various lichens, including Usnea in the temperate forests.As we found out, THg in lichens from the coast, where sea fog is observed, was 0.170 ± 0.017 mg kg−1 (n = 30), which is 2.6 times higher than the average value for inland areas (0.065 ± 0.004 mg kg−1 (n = 24) (Fig. 1A). The absolute values of THg in Usnea lichens from the coast in the south of the Russian Far East turned out to be higher than in the Ramalina menziesii lichens (from the same order Lecanorales and the same ecological form as Usnea) from the coast in California 3 (0.138 ± 0.012 mg kg−1). It is possible that such differences are related to species-specific features of their thalli. Different species of lichens from the same locality can accumulate different amounts of toxic substances in their thallus. Thus, Usnea contained 0.170 ± 0.017 mg kg−1, and the mesomorphic evernia (Evernia mesomorpha) collected on the same site—0.292 mg kg−1(n = 2).Figure 1Map of sampling sites and mean values of (A) THg concentrations in lichen (Usnea sp.) (site names correspond to data in Table 1), (B) THg in tiger fur. The blue triangles and circles represent the samples from coastal sub-region and the black—from inland sub-region. Lichen sampling was done in 2019 and tiger fur sampling was done in 2004–2014. The map was generated in Adobe Photoshop CS6, based on a map from the public domain on the site https://yandex.ru/legal/maps_termsofuse/?lang=en.Full size imageWeiss-Penzias et al. 3 do not give the average THg for inland lichens, but they show that the high MMHg content in lichens on the coast is obtained through coastal marine atmospheric fog. We compared our data with those for THg on Bathurst Island 22, where the spatial pattern in THg enrichment was very similar to that of MMHg, with enrichment highest at coastal sites and decreasing within 10 km, suggesting similar origins of atmospheric THg and MMHg to lichens. Potential sources of inorganic Hg and MMHg to lichens are diverse (e.g. 3,22). MMHg from THg can range from 4.4 to 23% 3, therefore special future studies are needed to understand the dynamics of Hg species in lichen.The average individual THg concentrations in tiger fur samples from the coast ranged from 0.115 to 0.918 mg kg−1 (n = 12), on average 0.434 ± 0.067 (Fig. 1B, Table 1), while tiger fur samples from the inland regions (n = 12) had lower concentrations of THg (range from 0.057 to 0.950 mg kg−1, average 0.239 ± 0.075); the differences between the means of the two sites were statistically significant (p = 0.01) (Fig. 1B, Table 1).Table 1 Statistics on the subsets of THg concentration data used in this paper.Full size tableThe average concentrations of THg in the tiger fur of two subregions were lower (0.434 ± 0.067 and 0.239 ± 0.075 mg kg−1) than similar values for pumas from California 3. In contrast to California, where the average THg content in pumas from the coastal area was three times higher than in animals from the inland areas, in the Russian Far East the average THg content in tiger fur sampled in the area influenced by the sea fog was two times higher than that in comparable samples from inland areas. Although, it is the inland area (the Amur River basin) that is subject to high levels of human activity, including the mining of coal and gold in the past and present, and we could expect higher levels of mercury in living components of ecosystems. The average concentrations of THg in the tiger fur were only from 0.056 to 0.232 mg kg−1 (n = 4) near coal and gold mining sites.Different age classes were sampled in both the coastal and inland areas, and THg concentrations increased with age (adult  > young) in both areas (Table 1). This pattern is typical for predatory animals in general and for pumas, in particular 3, which is natural due to the cumulative effect and increasing mercury content with age 22,23,24,25. Differences in the average individual mercury content in individual fur samples of young and adult tigers were significant (p = 0.04) (Table 1). Moreover, the differences between the average mercury content in young and adult tigers were insignificant in the inland site (p = 0.32), while being significant on the coast (p = 0.04) (Table 1).We did not observe any significant differences in THg concentrations between the sexes (p = 0.86) (Table 1) and between males from the coast and the inland (p = 0.25) (Table 1) as was noted earlier for puma 3,21. On the contrary, the average values of mercury in the fur of individuals from the coast were 3.1 times higher than from the inland sites within the group of females, this difference was statistically significant (p = 0.03) (Table 1). These data contradict to what was observed in puma by Weiss-Penzias et al. 3. Such feature of female tiger is apparently associated with their shorter migration routes and smaller individual territories compared to males 26,27. Unlike males, which can cross the main Sikhote-Alin ridge, females are usually located either on the territory in the zone of sea fog influence, or in the inland areas.In addition, young females often remain within the territory of their mothers during dispersal 15. Rather similar information was obtained for the wild European cat 28, where THg in females was about 1.4 times higher than in males, although the differences were not statistically significant. There were no significant differences in THg content between young tigers in coastal and inland areas, as well as in the samples of animals, which died in autumn–winter and spring-early summer (Table 1).Apparently, preying on land animals does not lead to the accumulation of high Hg levels in felids. The average Hg levels in the fur of a near-water species such as the ocelot (Felis pardalis) mainly preying on terrestrial animals, varied in the same range as the tiger: 0.5–1.25 mg kg−1 29. Tigers are mainly consumers of the second level and therefore the average content of mercury in their body (0.383 ± 0.062) (Table 1) is lower than in the fur of consumers of the third level such as the pine marten 1.80 ± 1.34 mg kg−1 30 or Daubenton’s bat 1.15 ± 0.27 mg kg−1 31.The only sample with a maximum mercury content of 1.402 mg kg−1 (age and gender unknown) was from the southwestern Primorye, which is located on the coast and where there are cinnabar deposits nearby. This sample was not used in the total analysis. Interestingly, the available sample of a young female Far Eastern leopard fur (Panthera pardus orientalis) from the same site had practically the same mercury content (1.456 mg kg−1). Local increased mercury content in the body of tigers can be associated with deposits of mercury-containing minerals. These data do not confuse our understanding of the sources of mercury in the ecosystem; they serve as a signal for a more profound study of natural processes.Our data on a higher mercury level (THg) in lichens and tigers of the sea coast compared to inland areas may be related to the effects of coastal sea atmospheric fog, a potential source of monomethylmercury (MMHg) produced in the ocean 3.The levels of mercury we found in Siberian tigers from the Russian Far East are about four times lower than the mercury content in the fur and vibrissa of puma from California 3. It seems that such differences are related to the position of these regions relative to the zones of deep faults of the mantle formation of the East Pacific platform 32. The maximum concentrations of Hg in the near-surface atmosphere are confined to such zones, and the concentrations decrease at a distance from them. California is located closer to such zones, while the south of the Russian Far East is further away.And the fact that different levels of mercury in ecosystems depend on the distance relative to the deep faults of the East Pacific platform is confirmed, for example, by pink salmon: fish from the Sea of Japan contain much less mercury than fish from the Kuril region closer to the fault zone (from 0.045 to 0.087 mg kg−1 wet weight 31. At the same time, we must not forget that California is the most populated and one of the most industrially developed states in the USA. However, it seems that natural processes currently play the main role in formation of heavy metal content in the discussed populations. Thus, lead concentrations in organs and tissues (liver, gonads, and muscle) of fish from Kuril oceanic waters was one and a half order of magnitude higher than that of pink salmon from the Sea of Japan 33.If the global anthropogenic mercury pollution of terrestrial and aquatic ecosystems continues, coastal food webs in the zone of influence of the East Pacific platform will be at most risk of toxicological effects. More

Abundant production of a type IV pilus in Synechococcus sp. WH7803We first detected an abundant PilA protein (i.e. SynWH7803_1795) in the extracellular proteomes of the model marine cyanobacterium Synechococcus sp. WH7803, accounting for up to 25% of the exoproteome19,20. Transmission electron microscopy confirmed the existence of the macromolecular pili structures (Fig. 1a and Fig. S1). Unlike Synechocystis sp. PCC6803 that simultaneously produces thick and thin pili14, this marine picocyanobacterium presented multiple pili of similar thickness (diameter of ~6 nm), each ~10 µm in length. The amino acid sequence of PilA revealed a typical Sec-targeting signal peptide and a conserved GFTLxE motif at the N-terminus of the protein (Fig. 1b) that is known to be cleaved in the cytoplasmic membrane by PilD before the protein is translocated to the base of the pili for assembly10. After cleavage, the N-terminal of PilA can be post-translationally modified, e.g. methylated, to increase the hydrophobicity and stability of the pilin10,21, although we were unable to detect this modified N-terminal tryptic peptide during proteomic analyses. In close proximity to pilA in the Synechococcus sp. WH7803 genome we found five other type IV-like pilin genes (Fig. 1c), all with the conserved GFTLxE motif (Fig. 1d).Fig. 1: Pilus in the marine cyanobacterium Synechococcus sp. WH7803.a Transmission electron microscopy images of wild-type Synechococcus sp. WH7803 (WT) and pili mutant (Δpili) obtained from late-exponential liquid cultures incubated in ASW medium under optimal growth conditions. Imaging of three independent cultures in different occasions consistently showed long pili appendages only in the wild-type strain (Fig. S1). Middle panel image, obtained with the same magnification as other panels, is from an intercellular region between wild-type cells to improve the visualisation of the pili. Scale bar represents 1 µm. b The amino acid sequence of PilA1 (SynWH7803_1795). Trypsin hydrolytic sites are indicated in blue. Red lines highlight tryptic peptides detected by shotgun proteomics. The conserved GFTLxE motif is shown and the cleavage site is indicated with an asterisk. c Genomic context of pilA1 in Synechococcus sp. WH7803. Numbers in each gene represent their ID number (SynWH7803_). In red are genes detected by proteomics. While PilA1 and PilE are abundantly detected in exoproteomes20, PilA2 has only ever been detected in cellular proteomes of this strain22. Blue dotted lines indicate genes encoding possible structural pilin pairs, i.e. PilA1-PilE, PilA2-PilV and PilA3-PilW. Question marks indicate genes encoding proteins of unknown function. d The N-terminal amino acid sequence of PilA1 and five other pilin-like proteins, all with the highly conserved GFTLxE motif. e Synechococcus sp. WH7803 structural pilus proteins identified by homology with S. elongatus PCC 794216 and assembled in the inner (IM) and outer membrane (OM) as modelled by Craig et al11. pilM and pilO were not identified by homology but the SynWH7803_2367 and SynWH7803_2365 genes are suggested because they form a standard pilMNOQ operon as found in other species. While S. elongatus PCC 7942 encodes three pilT, only two were found in Synechococcus sp. WH7803, one being part of the characteristic pilCTB operon.Full size imageUsing the pilus apparatus from the freshwater cyanobacterium S. elongatus PCC 7942 as a reference16 and the established architecture for type IV pilus machinery11, we were able to find all components necessary for pilus assembly in Synechococcus sp. WH7803 (Fig. 1e). We speculate that the genetic cluster encoding the six pilin-like proteins (Fig. 1c and 1d) may provide three distinct pili functions. Based on homology with the annotated genes from S. elongatus16 and conserved domains found using the CD-search tool in NCBI, we suggest the three pilin pairs: PilA1-PilE, PilA2-PilV and PilA3-PilW (Fig. 1c). Of these, shotgun proteomic analyses have only ever detected PilA1-PilE19,20 implying these are responsible for the pili observed in Fig. 1a, although PilA2 was also detected in low abundance in cellular—but not extracellular—proteomic datasets22. Unlike in S. elongatus, where PilA1 and the contiguously-encoded pilin-like protein are almost identical, the amino acid sequence of PilA1 and PilE in Synechococcus sp. WH7803 are clearly distinguishable. Although in much lower abundance, PilE seems to be correlated with PilA1 in the exoproteomes of this cyanobacterium19,20 and, therefore, it is possible that PilE and PilA1 form subunits of the same pilus apparatus.Pilus distribution amongst picocyanobacterial isolates and Single-cell Assembled Genomes (SAGs)Genomic analysis of sequenced marine picocyanobacterial isolates downloaded from the Cyanorak database23 revealed that 74% of sequenced Synechococcus (n = 46) and 33% of Prochlorococcus (n = 43) encoded pilA1 (Fig. 2 and Supplementary Data 1). In Synechococcus, pilA1 was prevalent in all clades (93%; n = 28) except for clades II and III where it was less abundant (44%; n = 18). Interestingly, all low light Prochlorococcus isolates from clades III and IV encoded pilA1 (n = 7; Fig. 2). Most of these pilA1-containing strains also encoded a pilE homologue in close proximity (Fig. 2). Genes pilA2 and pilA3 were also abundantly found in Synechococcus (59 and 74%, respectively), although were much less prevalent in Prochlorococcus (12 and 9%, respectively). As expected, all strains that encode at least one of the pilA types also possessed the transmembrane pilus apparatus, whereas this apparatus was completely absent or partially lost in strains lacking pilA (Fig. 2). PilA3 is known to be involved in DNA uptake and competence in S. elongatus, requiring additional competence proteins to do so16. Marine picyanobacteria are not known for being naturally competent but, interestingly, all strains encoding PilA3 also contained the competence genes encoding ComEA and ComEC (Fig. 2). Further work is needed to investigate the conditions under which the PilA3-type pilus becomes active in these organisms and, therefore, when exogenous DNA might be taken up.Fig. 2: The presence of pilus-related proteins in cultured marine picocyanobacteria strains.Pilus proteins from Synechococcus sp. WH7803 were used for the BLASTp search. Log10 E-value scales are shown (1 to More