Philippa Kaur

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

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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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

Restoring the connection between people and the rest of nature hinges on whole-system science, actions and negotiations.
Those who think about and practise sustainability are constantly looking for holistic interpretations of the world and are trying to understand systemic relations, networks and connections. Biodiversity has all of these things. It shows how every species needs other species to exist and thrive. It shows that all living organisms are part of a sophisticated and fascinating system made up of myriads of links. And humans are undoubtedly a part of it.
Credit: Pulsar Imagens / Alamy Stock PhotoIn the realm of sustainability, experts also ponder about time: how can life exist and thrive over time? Indeed, the above mentioned fascinating system evolves over time. And, over time, it has to adapt to unexpected change. It does that well when it is healthy, and less well when it is ill and constantly disturbed.For a long time, man-made impacts kept accumulating almost completely unchecked by societies, until the consequences for human well-being became untenable. Nowadays, environmental crises make the headlines regularly. They are nothing but the result of a broken connection between people and the rest of nature.Climate change is one major outcome of the broken human–rest of nature connection and has wide ramifications for both people and the planet. We now face imminent disaster, unequally across the world, yet addressing climate change remains an incredibly thorny task. Country representatives from most nations around the world meet regularly at the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCC) — most recently at COP27, which was held in Egypt — to continue the debate on what actions are needed to move the climate agenda forward, all while disasters continue to hit the most vulnerable populations. The world has seen 27 COP meetings to the UNFCC so far; one wonders how many more meetings will be needed to see real change happen.Interestingly, country representatives also meet regularly to discuss biodiversity protection; biodiversity decline — the other major consequence of the broken human–rest of nature connection — is just as worrying, with severe and ramified implications that are still largely underappreciated by decision-makers. These gatherings are the COP meetings to the Convention on Biological Diversity (CBD). Last year, we wrote about the then forthcoming COP15 to the CBD (Nat. Sustain. 4, 189; 2021), the meeting in which the new conservation targets to be met by 2030 were to be agreed. We highlighted the extent to which experts worried that those new targets might not go far enough. The meeting was postponed more than once due to the COVID-19 pandemic, and it is finally happening on 7 December 2022, in Montreal, Canada. The world has already seen 15 COP meetings to the CBD, how many more meetings will be needed for the biodiversity crisis to be averted?But let’s go back to thinking about sustainability. Experts look for holistic visions of the world. Here is an interesting example of what holism means. Biodiversity decline and climate change are both the result of the broken connection between people and the rest of nature, they ultimately have the same, deep roots. They are mutually reinforcing phenomena: unhealthy biodiversity contributes to climate change, and climate change makes biodiversity ill. All this is bad news for human and planetary well-being. The climate–biodiversity conundrum, at least to some degree, has been recognized at a higher level — during COP27, leaders dedicated one day to biodiversity.Yet, given that these issues are highly interconnected and have the same origin, why is the world insisting on discussing them as separate agendas? Why are we still holding two separate COPs? How are these meetings going to promote any fruitful synergy? How will they lead people to reconnect with the rest of nature? Country representatives should be breaking silos, embracing holism and bringing these intertwined issues, and their multiple ramifications, to the same negotiating table.Nature Sustainability welcomes the long-awaited COP15 to the CBD and hopes that countries will agree on feasible yet ambitious 2030 targets to protect and enhance biodiversity. But most of all, we hope that all of the experts and leaders involved in addressing the environmental crises embrace holism to promote meaningful actions across the world aimed at restoring people’s connection with the rest of nature. We are eager to see progress to this end. In the meantime, the collection we started in March 2021 with Nature Ecology & Evolution has been updated to renew our support to the biodiversity community. More

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RESEARCH BRIEFINGS
07 December 2022

Ancient environmental DNA from northern Greenland opens a new chapter in genetic research, demonstrating that it is possible to track the ecology and evolution of biological communities two million years ago. The record shows an open boreal-forest ecosystem inhabited by large animals such as mastodons and reindeer. More

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