Ecology

Predictors of reservoir statusOur analyses include all known rodent reservoirs for zoonotic pathogens (282 species). These reservoirs harbour a total of 95 known zoonotic pathogens (34 viruses, 26 bacteria, 17 helminths, 12 protozoa and six fungi) employing all known modes of transmission (43 vector-borne, 32 close-contact, 28 non-close contact, and 13 using multiple transmission modes) (Supplementary Data 2). Compared to presumed non-reservoirs (species currently not known to harbour any zoonotic pathogens), we observed that reservoir rodents are strikingly synanthropic (Figs. 2, 3a, Table 1). Despite potential geographic biases, and the general possibility that synanthropic species are better studied compared to non-synanthropic species (see Sampling bias and Supplementary Figs. 1, 2), synanthropy emerged as a defining characteristic of nearly all (95%) currently known rodent reservoirs. Of the 155 synanthropic species, only six are considered as truly synanthropic, i.e., predominately, if not exclusively, occurring in or near human dwellings, while the remaining species only occasionally show synanthropic behaviour (Supplementary Data 1).Fig. 2: Predictors of reservoir status.Final structural equation model linking reservoir status of rodent species (n = 269) with their synanthropy and hunting status, population fluctuations (s-index, log-transformed), and adult body mass, controlling for their occurrence in a range of habitats and the number of studies available per species. One-sided (directional) arrows represent a causal influence originating from the variable at the base of the arrow, with the width of the arrow and associated value representing the standardised strength of the relationship. The small double-sided arrows and numbers next to each response (endogenous) variable represent the error variance.Full size imageFig. 3: Characteristics of reservoir and synanthropic rodents.a Reservoir rodents are predominately synanthropic (n = 436 with n (non-reservoir) = 154, n (reservoir) = 282). b Synanthropic rodents display high population fluctuations (high s-index) (n = 269) and c, occur in multiple artificial habitats (n = 269) (Tables 1–3). In a, estimated probability and 95% confidence intervals are shown and in b–c, estimated probability is shown and shaded areas show 95 % confidence intervals.Full size imageTable 1 Summary of best-fit generalized linear mixed effects model for reservoir status (n = 436)Full size tableCompared to non-reservoirs, we also found that rodent reservoirs are disproportionately exploited by humans (hunted for meat and fur). Seventy-two of the regularly hunted rodent species (n = 83) are reservoirs (87%), and hunted rodent species harbour on average five times the number of zoonotic pathogens than non-hunted species (Table 2).Table 2 Summary of rodent characteristics divided by rodent group with respect to hunting, reservoir status, and synanthropic behaviourFull size tableWe explored causal pathways using a structural equation model (SEM) linking synanthropy, reservoir status, and their hypothesized predictors. The final model, which we established a priori, had 17 free parameters and 21 degrees of freedom (n = 269). The model fit, based on the SRMR (standardized root mean squared residual) and the RMSEA (root mean squared error of approximation) indicated a good fit (see Methods). From the initially formulated full model, the pathways linking reservoir status to population fluctuations (s-index, Methods), occurrence in grasslands, number of artificial habitats a species occurs in, and number of studies found per species were not significant and thus removed from the final model (Supplementary Fig. 3). Similarly, pathways linking synanthropy and occurrence in grasslands were not significant and also removed. All reported coefficients for pathways are standardized to facilitate comparisons among the different relationships. The relationships and coefficients below all refer to those in the final model.The focal variable in the model was reservoir status, which was strongly and positively associated with synanthropy and had the highest estimated pathway coefficient (standardised estimate = 0.58, 95% CI 0.49–0.66, Fig. 2). Controlling for synanthropy, species were more likely to be a reservoir with increasing adult weight (0.13, 0.04–0.22). Species that occur in savanna were less likely to be reservoirs (−0.13, −0.22 to −0.04), while hunted species were more likely to be reservoirs (Fig. 2, 0.20, 0.11–0.30).Synanthropy was influenced by four habitat variables: a species was more likely to be synanthropic if it occurs in a higher number of artificial habitats (0.17, 0.04–0.31), and occurs in urban areas (0.14, 0.01–0.27), deserts (0.12, 0.01–0.23), or forests (0.13, 0.02–0.24). Notably, species with higher s-index, and thus larger population fluctuations, were more likely to be synanthropic (0.12, 0.01–0.22), and the s-index itself decreased as adult weight increased (−0.16, −0.27 to −0.04). Finally, hunted species were characterized by higher adult bodyweight (0.35, 0.25–0.44) (Fig. 2).The number of studies per species was positively associated with both a species’ synanthropic behaviour (0.29, 0.19–0.39) and its reservoir status (0.09, 0.00– 0.19), albeit with weaker evidence for the latter effect (p = 0.054) (Fig. 2),The confirmatory generalized linear mixed effects models (GLMMs) (Tables 1, 3), which control for correlation among species within the same family, showed that our SEM results were robust. Indeed, synanthropy was a significant predictor of reservoir status. These models underscore synanthropy as the most important predictor of reservoir status in our analysis (Table 1, Figs. 2–3).Table 3 Summary of best-fit generalized linear mixed effects model for synanthropic status (n = 269)Full size tablePopulation fluctuations affect transmission riskOur newly compiled data on the magnitude of population fluctuations enabled comparative investigations beyond theoretically straightforward predictions that transmission risk increases with reservoir abundance for density-dependent systems. We show that while strong population fluctuations (measured as the s-index) are found frequently in both reservoir and non-reservoir rodents (Table 2), synanthropic rodents exhibit much larger population fluctuations compared to non-synanthropic rodents (Table 2, Figs. 2–3). This pattern was apparent despite broad confidence intervals in the relationship between the s-index and the probability of being synanthropic (Fig. 3b, Tables 2, 3). Taken together, our results suggest that larger population fluctuations in reservoir species increase zoonotic transmission risk via synanthropic behaviours of rodents, thereby increasing the likelihood of zoonotic spillover infection to humans.Habitat generalism and habitat transformation increase transmission riskWe also find that reservoir species thrive in human-created (artificial) habitats (Fig. 3a, c, Tables 2–3), which reflects a general flexibility in their use of diverse habitat types compared to non-reservoir species (Fig. 4a, Table 2). In addition, the number of zoonotic pathogens harboured by a rodent species increased with habitat breadth (r436 = 0.34, p  More

Bacterial strains and cultivationAll marine bacteria used in this study were cultivated using the ½ YTSS (yeast-tryptone-sea salt) medium (DSMZ 974), containing yeast extract 2 g/L, tryptone 1.25 g/L and Sigma sea salts 20 g/L or the defined marine ammonium mineral salt (MAMS) medium (DSMZ 1313) where HEPES (10 mM, pH 8.0) replaced the phosphate buffer [16]. All cultures were grown at 30 °C aerobically in a shaker (150 rpm).For growth competition assays between the WT and the olsA mutant, cultures of bacteria were grown in 10 mL ½ YTSS medium for the WT strain, or with the addition of 10 µg/mL gentamicin for the olsA mutant since a gentamicin cassette was inserted to construct the mutant [4]. Cells were harvested at mid-late exponential phase and diluted to an optical density measured at 540 nm (OD540) of 1.0. These cells were then both inoculated at 1% (v/v) into 250 mL flasks containing 50 mL growth media (either ½ YTSS or MAMS + 0.5 mM Pi) in triplicate and grown at 30 °C with shaking at 140 rpm. At time point 0 h, 100 µL samples were removed in triplicate from each flask. These samples were then ten-fold serially diluted in the same growth media to a dilution of 10−9. From each serial dilution tube, 10 µL droplets were pipetted in triplicate onto agar plates containing either ½ YTSS agar (to count both the WT and the olsA mutant) or ½ YTSS agar + 10 µg/mL gentamicin (to count just the olsA mutant). Once the droplets were dry, plates were incubated at 30 °C for 3-4 days. Colony forming units (CFU) were determined by counting the number of colonies in the dilution number where single colonies were clearly visible. For the cultures grown in ½ YTSS medium, samples were removed and enumerated using the same method at time points 24 h and 96 h. For the cultures grown in MAMS media + 0.5 mM Pi, samples were removed and enumerated at time points 0 h, 48 h and 96 h.Membrane separation by sucrose density gradient ultracentrifugationThe WT strain and the olsA mutant were grown in ½ YTSS medium to OD540 ~0.8. One litre of culture was then collected by centrifugation at 12,300 × g at 4 °C for 10 minutes, using a JLA 10.5 rotor. Cells were washed and resuspended in 50 mL HEPES buffer (pH 8.0, 10 mM). Cells were then pelleted by centrifugation at 4,500 × g at 4 °C for 10 min, before resuspending the pellet in 3 mL HEPES buffer (pH 8.0, 10 mM), containing 1.6X cOmplete Protease Inhibitor cocktail (Roche), 3X DNAse I buffer (NEB) and 6 units/mL DNase I (NEB). Cells were then lysed using a French Press at 1000 PSI. Cell debris was removed by centrifugation at 4,500 × g at 4 °C for 10 min and the supernatant was transferred to a new Oakridge centrifuge tube for pelleting total membranes by centrifugation at 75,600 × g at 10 °C for 45 min in a JA25.5 rotor. Pelleted membranes were then washed and resuspended in 20% (w/v) sucrose in HEPES buffer (10 mM, pH 8.0). Resuspended membrane samples were then layered on top of a stepwise gradient containing 3.3 mL 73% (w/v) sucrose at the bottom and 6.7 mL 53% (w/v) sucrose in between. Inner (IM) and outer (OM) membranes were separated by centrifugation at 140,000 × g at 4 °C, for 16 hours in a SW40-Ti rotor. The IM resided in the interface between the 53% (w/v) and 20% (w/v) sucrose layers and the OM in the interface between the 53% (w/v) and 73% (w/v) sucrose layers. Both IM and OM samples were removed from the sucrose density interface, diluted with 30 mL HEPES buffer (10 mM, pH 8.0), and pelleted by centrifugation at 75,600 × g for 45 min. IM and OM were then resuspended in 1 mL of the same HEPES buffer before lipid and protein extractions.Proteomics sample preparation, in-gel digestion and nanoLC-MS analysisIM and OM samples were carefully dissolved in 100 μL 1X LDS loading buffer (Invitrogen) before loading on a precast Tris-Bis NuPAGE gel (Invitrogen) using 1X MOPS running solution (Invitrogen). SDS-polyacrylamide gel electrophoresis was run for approximately 5 min to purify polypeptides in the polyacrylamide gel by removing contaminants. Polyacrylamide gel bands containing the membrane proteome were excised and digested by trypsin (Roche) proteolysis. The resulting tryptic peptides were extracted using formic acid-acetonitrile (5%:25%, v/v) before resuspension in acetonitrile-trifluoroacetate (2.5%:0.05%, v/v). Tryptic peptides were separated by nano-liquid chromatography (nanoLC) using an Ultimate 3000 LC system with an Acclaim PepMap RSLC C18 reverse phase column (ThermoFisher) at the Proteomics Research Technology Platform (PRTP) at the University of Warwick. MS/MS spectra were collected using an Orbitrap Fusion mass spectrometer (ThermoFisher) in electrospray ionization (ESI) mode. Survey scans of peptides from m/z 350 to 1500 were collected for each sample in a 1.5-hr LC-MS run. This resulted in 12 mass spectra (3 biological replicates of IM and OM of WT and the olsA mutant) with a total of ~ 7.5 G of MS/MS data.MS/MS data search and statistical analysesCompiled MS/MS raw files were searched against the genome of Ruegeria pomeroyi DSS-3 using the MaxQuant software package [17, 18]. Default settings were used and samples were matched between runs. The software package Perseus (v1.6.5.0) was used to determine differentially expressed proteins with a false discovery rate (FDR) of 0.01 [19]. The LFQ (label-free quantitation) intensity of each protein was normalized by dividing the total peptide intensity of each sample by the length of each protein. Peptides were retained for further analyses only if they were consistently found in all three biological replicates in at least one set of the four samples (IM_WT, IM_olsA, OM_WT, OM_olsA). Missing values were imputed using the default parameters (width, 0.3; down-shift 1.8) and statistical analyses were performed using a two-sample Student’s t-test. Principle component analysis (PCA) plots and volcano plots were generated using default settings in the Perseus package.To analyse the pathways of differentially expressed proteins between the wild-type and the mutant, the sequences of those proteins that were significantly overrepresented (FDR  More

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Department of Anthropology, East Carolina University, Greenville, NC, USARyan SchachtDepartment of Environmental Science, Policy and Management and Museum of Vertebrate Zoology, University of California, Berkeley, CA, 94720, USASteven R. BeissingerDepartment of Ecology and Evolution, University of Lausanne, 1015, Lausanne, SwitzerlandClaus WedekindEcology & Evolution, Research School of Biology, The Australian National University, Acton, Canberra, 2601, AustraliaMichael D. JennionsMARBEC Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, FranceBenjamin GeffroyELKH-PE Evolutionary Ecology Research Group, University of Pannonia, 8210, Veszprém, HungaryAndrás LikerBehavioural Ecology Research Group, Center for Natural Sciences, University of Pannonia, 8210, Veszprém, HungaryAndrás LikerBehavioral Ecology and Sociobiology Unit, German Primate Center, Leibniz Institute of Primate Biology, 37077, Göttingen, GermanyPeter M. KappelerDepartment of Sociobiology/Anthropology, University of Göttingen, 37077, Göttingen, GermanyPeter M. KappelerGroningen Institute for Evolutionary Life Sciences, University of Groningen, 9747 AG, Groningen, The NetherlandsFranz J. WeissingDepartment of Anthropology, University of Utah, Salt Lake City, UT, USAKaren L. KramerInstitute of Global Health, University College London, London, UKTherese HeskethCentre for Global Health, Zhejiang University School of Medicine, Hangzhou, P.R. ChinaTherese HeskethIHPE Univ Perpignan Via Domitia, CNRS, Ifremer, Univ Montpellier, Perpignan, FranceJérôme BoissierStockholm University Demography Unit, Sociology Department, Stockholm University, 106 91, Stockholm, SwedenCaroline UgglaKem C. Gardner Policy Institute, David Eccles School of Business, University of Utah, Salt Lake City, UT, USAMike HollingshausMilner Centre for Evolution, University of Bath, Bath, BA2 7AY, UKTamás SzékelyELKH-DE Reproductive Strategies Research Group, Department of Zoology and Human Biology, University of Debrecen, H-4032, Debrecen, HungaryTamás Székely More

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The isotopic spread for each study siteThe overall linear relationship between δ2H and δ18O values for hair (n = 81) and toenails (n = 39), respectively, were (Fig. 2):$$delta^{2} {text{H}}_{{text{hair(VSMOW)}}} = , 0.89 times delta^{18} {text{O}}_{{text{hair(VSMOW)}}} {-} , 86.16,;{text{R}}^{2} = , 0.19,;p , < , 0.01$$ (1) $$delta^{2} {text{H}}_{{text{toenail(VSMOW)}}} = , 0.15 times delta^{18} {text{O}}_{{text{toenail(VSMOW)}}} {-} , 91.69,;{text{R}}^{2} = , 0.00,;p , = , 0.69$$ (2) Figure 2δ2H and δ18O values (‰) of all samples for both hair (δ2H: n = 81, δ18O: n = 82) and toenails (δ2H and δ18O: n = 39). The solid black line represents the Global Meteoric Water Line (GMWL) [δ2H = 8 (times) δ18O + 10] and is included in the graph for comparison purposes. The regression lines between oxygen and hydrogen values for hair [δ2Hhair(VSMOW) = 0.89 × δ18Ohair(VSMOW) − 86.16, R2 = 0.19, p  − 82‰ were then split further where any samples with δ2Hhair values less than − 73‰ were initially classified as Site 2. These samples were then split again to either Site 2 (δ2Hhair ≥ 76‰) or Site 4 (δ2Hhair  − 73‰ were classified as Site 4. No samples could be classified as originating from Site 3. The second CART model was built for stable hydrogen and oxygen isotopes of toenails (Model 2) (Fig. 5b). The model included only two decision nodes in which the first predictor variable was δ2Htoenail value, where samples with values less than − 93‰ were predicted to be from Site 1. For toenail samples with hydrogen values greater than − 93‰, oxygen values were used to determine whether they could be classified as Site 2 or Site 4. Those samples with δ18Otoenail values less than 9.6‰ were classified as Site 2 and those with values greater than 9.6‰ were predicted as Site 4. No samples were predicted to be from Site 3 purely from stable hydrogen and oxygen isotopes in toenails. Finally, the third model consisted of stable hydrogen and oxygen isotope values in both hair and toenail samples (Model 3) (Fig. 5c). Model 3 selected toenails as the best attribute for classification, which indicates that toenail isotope values are the better predictor when both hair and toenail samples are present for analysis from Sites 1–4. The model was similar to that of Model 2.Figure 5Decision trees developed from both δ2H and δ18O values of (a) hair [Model 1, trained with n = 65], (b) toenails [Model 2, trained with n = 32] and (c) of both hair and toenails [Model 3, trained with n = 28]. The predicted study site numbers are shown on the first row within each bubble. The proportions of samples in each node are shown as decimals for Sites 1, 2, 3, 4, respectively. The percentages indicate the proportion of samples within each sub-partition.Full size imageConfusion matrices (Table 1) were constructed for all three models to evaluate the performance of the classification models. Of the three models, Model 3 proved to be the most accurate model with an overall accuracy of 71.4% (see Supplementary Fig S2. online). The performance evaluation summary, including measures for sensitivity, specificity, positive predictive value, and negative predictive value for all three models, is provided in (see Supplementary Table S3. online).Intra-individual differencesBoth hair and toenail samples were retrieved from 35 of the 86 individuals. The paired difference between δ2H values in hair and toenails of the same individual was tested using the Wilcoxon Signed Rank's test for non-normal data as the dataset failed the Shapiro–Wilk's normality test at the α = 0.05 significance level. Significant differences were found between δ2H values of hair (n = 35, mean = − 78.0‰, s.d. = 3.06) and toenails (n = 35, mean = − 90.9‰, s.d. = 3.27) from the same individual (p  0.05. Overall, the isotopic values of δ2H in hair were higher than those of toenail from the same individual by 13.0‰, on average, with a standard deviation of 8.4‰. For δ18O, the average was 1.5‰ with a standard deviation of 4.6‰ (Fig. 6).Figure 6(a) δ2H and (b) δ18O values in hair and toenails for all individuals that provided both tissue types (n = 35). Study site information are also shown by shapes. The standard deviations of each sample, ran in either duplicates or triplicates, are shown by error bars. Note that error bars cannot be seen for some samples due to small standard deviations. The average difference between the isotopic values of hair and toenail from the same individual were 13.0‰ with a standard deviation of 8.4‰ for δ2H and 1.5‰ with a standard deviation of 4.6‰ for δ18O.Full size imageThe linear relationships between δ2H in hair and toenails for all individuals were (see Supplementary Fig S3. online):$$delta^{2} {text{H}}_{{{text{hair}}}} = , 0.48 times delta^{2} {text{H}}_{{text{toenail }}} {-} , 34.72,;{text{R}}^{2} = , 0.16,;p , < , 0.05$$ (19) and for δ18O:$$delta^{18} {text{O}}_{{{text{hair}}}} = , 0.55 times delta^{18} {text{O}}_{{{text{toenail}}}} + , 5.16,;{text{R}}^{2} = , 0.13,;p , < 0.05$$ (20) Overall, both equations showed a weak relationship, as seen by the small R2 values. More

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