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    Evolutionary diversification of methanotrophic ANME-1 archaea and their expansive virome

    Sampling and incubationFour rock samples were collected from the 3.7 km-deep Auka vent field in the Southern Pescadero Basin (23.956094N, 108.86192W)20,23. Sample NA091.008 was collected in 2017 on cruise NA091 with the Eexploration vessle Nautilus and incubated as described previously34. Samples 12,019 (S0200-R1), 11,719 (S0193-R2) and 11,868 (S0197-PC1), the latter representing a lithified nodule recovered from a sediment push core, were collected with Remotely operated vehicle SuBastian and Research vessel Falkor on cruise FK181031 in November 2018. These samples were processed shipboard and stored under anoxic conditions at 4 °C for subsequent incubation in the laboratory. In the laboratory, rock samples 12,019 and 11,719 were broken into smaller pieces under sterile conditions, immersed in N2-sparged sterilized artificial sea water and incubated under anoxic conditions with methane, as described previously for NA091.008 (ref. 34). Additional sampling information can be found in Supplementary Table 1. Mineralogical analysis by X-ray Powder Diffraction (XRD) identified barite in several of these samples, collected from two locations in the Auka vent field, including on the western side of the Matterhorn vent (11,719, NA091.008), and one oil-saturated sample (12,019) recovered from the sedimented flanks from the southern side of Z vent. Our analysis also includes metagenomic data from two sediment cores from the Auka vent field (DR750-PC67 and DR750-PC80) collected in April 2015 with the ROV Doc Ricketts and R/V Western Flyer (MBARI2015), previously published (ref. 23).Fluorescence in situ hybridizationSamples were fixed shipboard using freshly prepared paraformaldehyde (2 vol% in 3× Phosphate Buffer Solution (PBS), EMS15713) at 4 °C overnight, rinsed twice using 3× PBS, and stored in ethanol (50% in 1× PBS) at −20 °C until processing. Small pieces ( More

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    Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems

    Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).Article 
    CAS 

    Google Scholar 
    DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).Article 
    CAS 

    Google Scholar 
    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).Article 

    Google Scholar 
    Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).Article 
    CAS 

    Google Scholar 
    IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Gazol, A. et al. Forest resilience to drought varies across biomes. Glob. Change Biol. 24, 2143–2158 (2018).Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Change Biol. 24, 504–516 (2018).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).Article 
    CAS 

    Google Scholar 
    Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

    Google Scholar 
    Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2019).Article 

    Google Scholar 
    Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).Article 

    Google Scholar 
    Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

    Google Scholar 
    Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).Article 
    CAS 

    Google Scholar 
    Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).Article 
    CAS 

    Google Scholar 
    Lian, X. et al. Seasonal biological carryover dominates northern vegetation growth. Nat. Commun. 12, 983 (2021).Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 
    CAS 

    Google Scholar 
    Jeong, S. J. et al. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sens. Environ. 190, 178–187 (2017).Article 

    Google Scholar 
    Zeng, Z. et al. Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere. Agric. Meteorol. 310, 108630 (2021).Article 

    Google Scholar 
    Kelsey, K. C. et al. Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities. Glob. Change Biol. 27, 1572–1586 (2021).Article 

    Google Scholar 
    Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).Article 

    Google Scholar 
    IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

    Google Scholar 
    Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).Article 
    CAS 

    Google Scholar 
    Zhang, Y. et al. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).Article 
    CAS 

    Google Scholar 
    Liu, Y. Y. et al. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).Article 

    Google Scholar 
    Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

    Google Scholar 
    Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).Article 
    CAS 

    Google Scholar 
    D’Andrea, E. et al. Unravelling resilience mechanisms in forests: role of non-structural carbohydrates in responding to extreme weather events. Tree Physiol. 41, 1808–1818 (2021).Article 

    Google Scholar 
    Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 24, 5176–5187 (2018).Article 

    Google Scholar 
    Xie, J. et al. Spring temperature and snow cover climatology drive the advanced springtime phenology (1991–2014) in the European Alps. J. Geophys. Res. Biogeosci. 126, e2020JG006150 (2021).Xie, J. et al. Altitude-dependent influence of snow cover on alpine land surface phenology. J. Geophys. Res. Biogeosci. 122, 1107–1122 (2017).Article 

    Google Scholar 
    Peng, S. et al. Change in winter snow depth and its impacts on vegetation in China. Glob. Change Biol. 16, 3004–3013 (2010).
    Google Scholar 
    Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).Article 

    Google Scholar 
    Angert, A. et al. Drier summers cancel out the CO2 uptake enhancement induced by warmer springs. Proc. Natl Acad. Sci. USA 102, 10823–10827 (2005).Article 
    CAS 

    Google Scholar 
    Musselman, K. N. et al. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Change 11, 418–424 (2021).Article 

    Google Scholar 
    Kreyling, J. Winter climate change: a critical factor for temperate vegetation performance. Ecology 91, 1939–1948 (2010).Article 

    Google Scholar 
    Bose, A. K. et al. Growth and resilience responses of Scots pine to extreme droughts across Europe depend on predrought growth conditions. Glob. Change Biol. 26, 4521–4537 (2020).Article 

    Google Scholar 
    Martinez-Vilalta, J. et al. Hydraulic adjustment of Scots pine across Europe. New Phytol. 184, 353–364 (2009).Article 
    CAS 

    Google Scholar 
    Klein, T. et al. Drought stress, growth and nonstructural carbohydrate dynamics of pine trees in a semi-arid forest. Tree Physiol. 34, 981–992 (2014).Article 
    CAS 

    Google Scholar 
    Kannenberg, S. A. & Phillips, R. P. Non-structural carbohydrate pools not linked to hydraulic strategies or carbon supply in tree saplings during severe drought and subsequent recovery. Tree Physiol. 40, 259–271 (2020).Article 
    CAS 

    Google Scholar 
    Karst, J. et al. Stress differentially causes roots of tree seedlings to exude carbon. Tree Physiol. 37, 154–164 (2017).CAS 

    Google Scholar 
    Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).Article 

    Google Scholar 
    Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).Wu, X. et al. Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere. Geophys. Res. Lett. 44, 6173–6181 (2017).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).Article 

    Google Scholar 
    Martin-Benito, D. & Pederson, N. Convergence in drought stress, but a divergence of climatic drivers across a latitudinal gradient in a temperate broadleaf forest. J. Biogeogr. 42, 925–937 (2015).Article 

    Google Scholar 
    Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).Article 

    Google Scholar 
    Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).Article 
    CAS 

    Google Scholar 
    Zhang, W. et al. Divergent response of vegetation growth to soil water availability in dry and wet periods over Central Asia. J. Geophys. Res. Biogeosci. 126, e2020JG005912 (2021).Article 

    Google Scholar 
    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

    Google Scholar 
    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

    Google Scholar 
    Liang, W. et al. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric. For. Meteorol. 204, 22–36 (2015).Article 

    Google Scholar 
    Zhang, Y. et al. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).Article 
    CAS 

    Google Scholar 
    Jones, M. O. et al. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011).Article 

    Google Scholar 
    Konings, A. G. et al. Interannual variations of vegetation optical depth are due to both water stress and biomass changes. Geophys. Res. Lett. 48, e2021GL095267 (2021).Article 

    Google Scholar 
    Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data 9, 791–808 (2017).Article 

    Google Scholar 
    Harris, I. et al. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    Barichivich, J. et al. Temperature and snow-mediated moisture controls of summer photosynthetic activity in northern terrestrial ecosystems between 1982 and 2011. Remote Sens. 6, 1390–1431 (2014).Article 

    Google Scholar 
    Vicente-Serrano, S. M., Begueria, S. & Lopez-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

    Google Scholar 
    Wieder, W. R. et al. Regridded Harmonized World Soil Database v1.2 (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1247Kottek, M. et al. World map of the Koppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).Article 

    Google Scholar 
    Jakubauskas, M. E., Legates, D. R. & Kastens, J. H. Harmonic analysis of time-series AVHRR NDVI data. Photogramm. Eng. Remote Sens. 67, 461–470 (2001).
    Google Scholar 
    Liu, Q. et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol. 22, 644–655 (2016).Article 
    CAS 

    Google Scholar 
    Fu, Y. H. et al. Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob. Ecol. Biogeogr. 23, 1255–1263 (2014).Article 

    Google Scholar 
    Jiang, P. et al. Enhanced growth after extreme wetness compensates for post-drought carbon loss in dry forests. Nat. Commun. 10, 195 (2019).Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Pham, L. T. H. & Brabyn, L. Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J. Photogramm. Remote Sens. 128, 86–97 (2017).Article 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

    Google Scholar 
    Li, Y. Code for ‘Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems’. GitHub https://github.com/leeyang1991/phenology-effects-on-drought-recovery (2022). More

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    Genome-wide identification and expression profile of Elovl genes in threadfin fish Eleutheronema

    Identification of Elovl genes from E. tetradactylum and E. rhadinumTotally, we successfully identified 9 Elovl genes, including elovl1a, elovl1b, elovl4a, elovl4b, elovl5, elovl6, elovl6l, elovl7a, and elovl8b, both from E. tetradactylum and E. rhadinum genome (Table 2). In E. rhadinum, the shortest and the longest putative CDS length among all Elovl genes was 810 bp and 2019 bp, respectively. Their encoded protein size ranged from 269 amino acids to 672 amino acids. The theoretical molecular weight of Elovl proteins varied from 31061.48 to 75051.42 Da, with the theoretical isoelectric points (pI) ranging from 7.86 to 9.59. Most of the Elovl proteins were characterized as stable and hydrophilic proteins. Signal peptide prediction analysis showed that the elovl1b, elovl5, and elovl6 contained signal peptide sequences. In addition to elovl8b, all Elovl proteins contained transmembrane domains ranging from 5 to 7. Almost all Elovl proteins were predicted to be endoplasmic reticulum-located except elovl8b, predominantly localized in the nucleus.Table 2 Basic information for the Elovl gene family members.Full size tableIn E. tetradactylum, the putative CDS length of Elovl genes ranged from 810 to 1824 bp, and their encoded protein size ranged from 269 amino acids to 409 amino acids. The molecular weight of Elovl proteins varied from 31049.42 to 68750.14 Da, with the pI ranging from 8.72 to 9.64. Like Elovl proteins in E. rhadinum, most elovl proteins were predicted to be stable and hydrophilic. Signal peptide prediction analysis revealed that elovl1a, elovl5, and elovl6 had signal peptide sequence, which was different from E. rhadinum that elovl1b contained signal peptide sequence, but elovl1a did not. In addition, seven members showed the same number of transmembrane structures with E. rhadinum, while the elovl8b contained three and elovl4b contained seven transmembrane structures in contrast to E. rhadinum. The elovl8b was predicted to be localized in nuclear, while other members were localized in the endoplasmic reticulum, similar to E. rhadinum.Evolution of divergence and conservation of Elovl genesDivergence and conservation accompany the process of species evolution. To elucidate the phylogenetic relationship of Elovl genes among different species, a maximum like-hood tree was constructed on the basis of 18 Elovl genes in E. tetradactylum and E. rhadinum and 106 publicly available Elovl protein sequences. As shown in Fig. 1, these Elovl genes can be divided into eight subfamilies, including elovl1a/1b, elovl2, elovl3, elovl4a, elovl5, elovl6/6 l, elovl7a/7b, elovl8a/8b. However, 6 subfamilies were presented in the Eleutheronema genus, and there was only one subtype for elovl7 (elovl7a) and elovl8 (elovl8b) in E. tetradactylum and E. rhadinum. The elovl3 was mainly identified in mammalians such as Homo sapiens and Mus musculus, while a recent study reported a full repertoire of Elovl genes in the Colossoma macropomum genome, including elovl330. The loss of elovl2 occurred in the vast majority of marine fish lineages, which was only presented in a few fish species, such as C. carpio, D. rerio, S. salar, and S. grahami.Figure 1Phylogenetic tree for 18 Elovl proteins from E. tetradactylum and E. rhadinum, and 106 publicly available Elovl proteins from other species. All these proteins were aligned using ClustalW and then subjected to MEGAX for phylogenetic tree construction using the maximum like-hood method with 1000 replicates.Full size imageWe further performed the gene structure analysis to visualize the exon–intron structure of each gene, and the results revealed that the elovl8b had the largest intron number, while the elovl6/6 l subfamily genes contained three introns (Fig. 2a). Except for elovl8, Elovl genes belonging to the same subfamily shared a similar gene structure. Additionally, we identified ten motifs in Elovl genes, and the conversed motif types, numbers, and distributions in Elovl proteins were much more similar except for the elovl8b (Fig. 2b, TableS1). Two conserved motifs were found in the Elovl gene family except for elovl8b in E. rhadinum, which were related to the ELO domain via SMART evaluation analysis (Fig. 2c and d). Gene structural variation is important for gene evolution. In E. tetradactylum and E. rhadinum, Elovl genes showed similar gene structure, and the proteins shared similar motif compositions, indicating that the Elovl genes were highly conserved in the Eleutheronema genus.Figure 2Gene structure and conserved motifs diagram of Elovl genes. (a) Gene structure of Elovl genes. Exons were represented by pink boxes and introns by black lines; (b) Conserved motifs of Elovl proteins; (c and d) Logo representations of the ELO domains, motifs 1 and 2, respectively.Full size imageIn the process of evolution via natural selection, adaptation to certain environmental conditions likely drove the changes in endogenous capacity for LC-PUFA biosynthesis between marine and freshwater fishes31. The Elovl gene family has been functionally studied and characterized in a variety of fish species, and the member of the Elovl gene family of each species varied greatly. In the present study, for a comprehensive analysis of Elovl genes in the Eleutheronema genus, the Elovl gene ortholog clusters of mammals and various teleosts with different ecological niches and habitats were collected. The results showed that only seven Elovl genes (one gene for each subtype) were observed in mammals; however, more members were variably presented in teleosts, which might be related to the teleost-specific duplication. A previous study revealed that Sinocyclocheilus graham and C. carpio possessed the highest number of Elovl genes, containing 21 members of subtypes, resulting from an extra independent 4th whole-genome duplication event32, 33. Interestingly, only 9 Elovl genes were observed in Eleutheronema genus, the same as T. rubripes, possibly due to gene loss and the asymmetric acceleration of the evolutionary rate in one of the paralogs following the whole-genome duplication in some teleost fishes34. Additionally, the elovl2 and elovl3 were absent, but a novel subtype, elovl8, was present in most marine fishes. The elovl8, the most recently identified and novel active member of the Elovl protein family member, has been proposed to be a fish-specific elongase with two gene paralogs (elovl8a and elovl8b) described in teleost35. In Eleutheronema, we also found that the elovl8b was presented in E. tetradactylum and E. rhadinum, indicating the important roles in the LC-PUFAs biosynthesis of Eleutheronema fish. Similar results were also observed in rabbitfish and zebrafish20. The Elovl gene family member number in Eleutheronema genus is the same as T. rubripes, but less than I. punctatus (10), Gadus morhua (10), D. rerio (14), S. salar (18), and C. carpio (21), which might be due to the differential expansion events during the evolutions of fish species.Predicting the protein structure is a fundamental prerequisite for understanding the function and possible interactions of a protein. In the present study, the secondary structures as well as three-dimensional structures of Elovl proteins in both E. tetradactylum and E. rhadinum were predicted using the SOPMA and Phyre2 programs, respectively. The protein structures of all the candidate Elovl proteins were modeled at  > 90% confidence. The secondary structures of these proteins in E. tetradactylum revealed 40.86–50.30% alpha helixes, 28.10–28.10% random coil, 13.75–20.67% extended strand and 2.38–4.47% beta turn, while these ratios were predicted to be 47.55–53.27, 30.00–36.01, 6.99–18.12 and 2.38–4.75%, respectively, in E. rhadinum (Table 3). High ratio of alpha helixes and random coil in the Elovl protein structure might play important roles in fatty acids biosynthesis in fish, in accordance with the literature for the order Perciformes in Perca fluviatilis36. Additionally, the secondary structure pattern of Elovl proteins in the candidate E. tetradactylum and E. rhadinum species were highly similar (Fig. 3), indicating the probable similar biological functions as well as highly evolutionarily conserved Elovl genes in Eleutheronema species.Table 3 Properties of the secondary structures of Elovl proteins.Full size tableFigure 3The secondary structure pattern, including alpha helix (blue color), random coil (purple color), extended strand (red color), and beta turn (green color), of Elovl proteins in E. tetradactylum and E. rhadinum.Full size imageThe 3D model results showed that all predicted Elovl proteins had complex 3D structures, composing of multiple secondary structures including alpha-helices, random coils, and others (Fig. 4). The Elovl proteins of different subfamilies showed different 3D configurations. The 3D structures of Elovl proteins also revealed the presence of the conserved domain in each Elovl protein, which showed a typical three-dimensional frame comprising of various parallel alpha-helixes. To assay the quality and accuracy of the predicted 3D model for the candidate Elovl proteins, the Ramachandran plot analysis was employed (Figure S1). In model validation, the qualities of the Elovl proteins model varied from 90 to 98% based on the Ramachandran plot analysis, suggesting the reasonably good quality and reliability of the predicted 3D models. These results indicated that the predicted 3D model of Elovl proteins could provide valuable information for the further comprehensive studies of molecular function in the fatty acids biosynthesis in Eleutheronema species. Additionally, the comparisons between these structures in E. tetradactylum and E. rhadinum suggested that the Elovl proteins encompassed the conserved structures. In addition, gene duplication resulted in obvious 3D structural variation in the duplicated genes, such as Elovl4 (elovl4a and elovl4b), Elovl6 (elovl6 and elovl6l). The ascertained variations were revealed in duplicated Elovl proteins, and the diversities in these proteins structure may reflect their different obligations in the fatty acid biosynthesis and other biological processes.Figure 4Three-dimensional modeling of Elovl proteins in E. tetradactylum and E. rhadinum. All models have confidence levels above 90%.Full size imageTo explore the functional selection pressures acting on Elovl gene family, Ka, Ks, and Ka/Ks ratios were calculated for each gene. Generally, Ka/Ks  1 indicates positive selection. In this study, we found that all the Ka/Ks ratios for each gene were less than 0.5, suggesting that they were subjected to strong purifying selection during evolution, and their functions might be evolutionarily conserved (Fig. 5). Therefore, theoretically, the Elovl genes in the Eleutheronema genus had eliminated deleterious mutations in the population through purification selection. Similar results were also observed in Elovl gene family of Gymnocypris przewalskii that no positive selection trace was detected in most members except elovl211. Moreover, elovl6l and elovl8b showed a higher average Ka/Ks ratio than the other seven members, indicating that the evolution of elovl6l and elovl8b might be much less conservative and thereby could provide more variants for natural selection in Eleutheronema species.Figure 5The evolutionary rates of the Elovl genes in (a) E. tetradactylum and (b) E. rhadinum. The Ka, Ks, and Ka/Ks values were demonstrated in boxplots with error lines.Full size imageChromosomal location, collinearity, and protein–protein interaction network analysis of Elovl genesAs shown in Fig. 6a and b, Elovl genes were randomly and unevenly distributed on seven chromosomes in both E. tetradactylum and E. rhadinum, including Chr5, Chr6, Chr8, Chr10, Chr11, Chr13, and Chr25. The Chr5 and Chr6 harbored two Elovl genes (elovl1b and elovl8b in Chr5, elovl5 and elovl6l in Chr6), while other chromosomes each carried a single Elovl gene. Collinearity relationship analysis was performed to further investigate the gene duplication events within the Elovl gene family. The results revealed that a pair of segmental duplication genes (elovl4a/4b) showed collinear relationships. A chromosome-wide collinearity analysis also showed that the chromosomes were highly homologous between E. tetradactylum and E. rhadinum, including the Elovl gene family (Figure S2). To infer the protein interaction within Elovl gene family, we constructed the protein–protein interaction (PPI) network of the Elovl proteins based on the interaction relationship of the homologous Elovl proteins in zebrafish. The results showed that Elovl genes had close interaction with other members except for the elovl4a/4b and elovl8b (Fig. 6c), which suggested that they might participate in diverse functions by interacting with other proteins. Thus far, elovl4a and elovl4b were widely identified in most fish, which could effectively elongate PUFA substrates37. In addition, the elovl4a/4b were identified to be homologous proteins of zebrafish, indicating that the elovl4 subtype was highly conserved during evolution and played important roles in the biosynthesis of LC-PUFA in Eleutheronema.Figure 6Chromosomal location and collinearity analysis of Elovl gene family in (a) E. tetradactylum and (b) E. rhadinum. Colored boxes represented chromosomes. Segmental duplication genes are connected with grey lines; (c) a protein–protein interaction network for Elovl genes based on their orthologs in zebrafish.Full size imageExpression patterns of ELOVL genes in different tissuesIn the present study, we aimed to determine the expression patterns and gained insights into the potential functions of Elovl genes in the brain, eye, gill, heart, kidney, liver, muscle, stomach, and intestine. The expression patterns of Elovl genes in different tissues and species were distinct, suggesting the diverse roles during fish development (Fig. 7a and b). In our present study, the elovl1a and elvovl1b were expressed in a relatively narrow range of tissues, including the liver, stomach, and intestine. Some Elovl genes had much higher relative expression rates, e.g., elovl1a and elovl7a. The elovl4a was primarily distributed in the brain and eye, slightly expressed in gills while hardly detectable in other tissues, consistent with previous studies37, 38, which might play an important role in endogenous biosynthesis of LC-PUFA in the neural system of fish. In contrast to elovl4a, elovl4b was ubiquitously, instead of tissue-specific, expressed in most tissues while hardly examined in the heart and kidney. The elovl4a and elovl4b were two commonly paralogues in evolutionarily diverged fish species, and the striking difference in expression patterns between elovl4a and elovl4b might be due to the potential functional divergence of these two paralogues. In addition, elovl8b, the novel active member of the Elovl protein family, was expressed in several tissues, suggesting the essential roles in LC-PUFAs biosynthesis of teleost as indicated by a previous study20. Moreover, the differences in expression patterns among different Elovl genes indicated that these genes might possibly undergo functional divergence during evolution in the Eleutheronema genus. Overall, our present study firstly provided the preliminary organ-specific expression data of the Elovl gene family in E. tetradactylum and E. rhadinum, which could provide the foundation for further clarifying the function of these genes in the evolutionary development of the Eleutheronema genus.Figure 7qPCR assessment of tissue distribution of elovl1a, elovl1b, elovl4a, elovl4b, elovl5, elovl6, elovl6l, elovl7a, and elovl8b gene expression in (a) E. tetradactylum and (b) E. rhadinum for various tissues including the brain, eye, gill, heart, kidney, liver, muscle, stomach, and intestine.Full size image More

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    Sleep deprivation among adolescents in urban and indigenous-rural Mexican communities

    Our main objective was to test the SJH (positing that adolescents living in “traditional”, non-industrial environments will more closely fulfil their “biological/natural” sleep requirements25,26) by comparing sleep deprivation among adolescents in rural and urban societies. The SJH argues that adolescent “biological/natural” sleep quotas and circadian cycles can be ascertained from free days, when sleep patterns are minimally shaped by social commitments5,37. Therefore, we predicted that sleep deprivation would be rare in the more rural agricultural settings of Puebla and Campeche but more frequent among participants in Mexico City. Likewise, we predicted that we would not see sleep deprivation on free days among any of the rural participants.Our predictions were not supported, instead, we found that short sleep quotas during school nights are common in both rural agricultural settings, with over 75% of adolescents in each group sleeping  More

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    Author Correction: Measuring the world’s cropland area

    Authors and AffiliationsStatistics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyFrancesco N. Tubiello, Giulia Conchedda, Leon Casse & Giorgia De SantisDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyHao Pengyu & Chen ZhongxinInternational Institute for Applied Systems Analysis, Laxenburg, AustriaSteffen FritzGeospatial Unit, Land and Water Division, Food and Agriculture Organization of the United Nations, Rome, ItalyDouglas MuchoneyAuthorsFrancesco N. TubielloGiulia ConcheddaLeon CasseHao PengyuChen ZhongxinGiorgia De SantisSteffen FritzDouglas MuchoneyCorresponding authorCorrespondence to
    Francesco N. Tubiello. More

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    A new technique to study nutrient flow in host-parasite systems by carbon stable isotope analysis of amino acids and glucose

    Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Lafferty, K. D., Dobson, A. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. 103, 11211–11216 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Amundsen, P. A. et al. Food web topology and parasites in the pelagic zone of a subarctic lake. J. Anim. Ecol. 78, 563–572. https://doi.org/10.1111/j.1365-2656.2008.01518.x (2009).Article 

    Google Scholar 
    Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).Article 

    Google Scholar 
    Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482. https://doi.org/10.1111/j.1600-0706.2013.00243.x (2013).Article 

    Google Scholar 
    Sato, T. et al. Nematomorph parasites drive energy flow through a riparian ecosystem. Ecology 92, 201–207 (2011).Article 

    Google Scholar 
    Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).Article 

    Google Scholar 
    Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2018).Article 
    CAS 

    Google Scholar 
    Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50, 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 

    Google Scholar 
    Barber, I. & Svensson, P. A. Effects of experimental Schistocephalus solidus infections on growth, morphology and sexual development of female three-spined sticklebacks Gasterosteus aculeatus. Parasitology 126, 359–367. https://doi.org/10.1017/s0031182002002925 (2003).Article 
    CAS 

    Google Scholar 
    Scharsack, J. P., Koch, K. & Hammerschmidt, K. Who is in control of the stickleback immune system: Interactions between Schistocephalus solidus and its specific vertebrate host. Proc. Biol. Sci. 274, 3151–3158. https://doi.org/10.1098/rspb.2007.1148 (2007).Article 

    Google Scholar 
    Hopkins, C. A. Studies on cestode metabolism. I. glycogen metabolism in Schistocephalus solidus In vivo. J. Parasitol. 36, 384–390 (1950).Article 
    CAS 

    Google Scholar 
    Körting, W. & Barrett, J. Carbohydrate catabolism in the plerocercoids of Schistocephalus solidus (Cestoda: Pseudophyllidea). Int. J. Parasitol. 7, 411–417 (1977).Article 

    Google Scholar 
    Hebert, F. O., Grambauer, S., Barber, I., Landry, C. R. & Aubin-Horth, N. Major host transitions are modulated through transcriptome-wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite. Mol. Ecol. 26, 1118–1130. https://doi.org/10.1111/mec.13970 (2017).Article 
    CAS 

    Google Scholar 
    Berger, C. S. et al. The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions. Parasites Vectors 14, 436. https://doi.org/10.1186/s13071-021-04933-w (2021).Article 
    CAS 

    Google Scholar 
    Jolles, J. W., Mazue, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282. https://doi.org/10.1038/s41598-020-69057-0 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Scharsack, J. P. et al. Climate change facilitates a parasite’s host exploitation via temperature-mediated immunometabolic processes. Glob. Change Biol. 27, 94–107. https://doi.org/10.1111/gcb.15402 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Kochneva, A., Borvinskaya, E. & Smirnov, L. Zone of interaction between the parasite and the host: Protein profile of the body cavity fluid of Gasterosteus aculeatus L. infected with the Cestode Schistocephalus solidus (Muller, 1776). Acta Parasitol. 66, 569–583. https://doi.org/10.1007/s11686-020-00318-8 (2021).Article 
    CAS 

    Google Scholar 
    Barber, I. & Scharsack, J. P. The three-spined stickleback-Schistocephalus solidus system: An experimental model for investigating host-parasite interactions in fish. Parasitology 137, 411–424. https://doi.org/10.1017/S0031182009991466 (2010).Article 
    CAS 

    Google Scholar 
    Weber, J. N., Steinel, N. C., Shim, K. C. & Bolnick, D. I. Recent evolution of extreme cestode growth suppression by a vertebrate host. Proc. Natl. Acad. Sci. U. S. A. 114, 6575–6580. https://doi.org/10.1073/pnas.1620095114 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: A neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).Article 
    CAS 

    Google Scholar 
    Hayes, J. M. Factors controlling 13C contents of sedimentary organic compounds: Principles and evidence. Mar. Geol. 113, 111–125 (1993).Article 
    ADS 
    CAS 

    Google Scholar 
    France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313 (1995).Article 
    ADS 

    Google Scholar 
    Post, D. M. Using stable isotopes to estimate trophic position: Models, methods and assumptions. Ecology 83, 703–718 (2002).Article 

    Google Scholar 
    O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326. https://doi.org/10.1007/s00442-017-3881-9 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 

    Google Scholar 
    Liu, H.-z, Luo, L. & Cai, D.-l. Stable carbon isotopic analysis of amino acids in a simplified food chain consisting of the green alga Chlorella spp., the calanoid copepod Calanus sinicus, and the Japanese anchovy (Engraulis japonicus). Can. J. Zool. 96, 23–30. https://doi.org/10.1139/cjz-2016-0170 (2018).Article 
    CAS 

    Google Scholar 
    Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem. 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).Article 
    CAS 

    Google Scholar 
    Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).Article 
    ADS 

    Google Scholar 
    Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fish. Res. 219, 105303. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

    Google Scholar 
    Choy, K., Smith, C. I., Fuller, B. T. & Richards, M. P. Investigation of amino acid δ13C signatures in bone collagen to reconstruct human palaeodiets using liquid chromatography–isotope ratio mass spectrometry. Geochim. Cosmochim. Acta 74, 6093–6111. https://doi.org/10.1016/j.gca.2010.07.025 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).Article 
    CAS 

    Google Scholar 
    Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid delta13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Honch, N. V., McCullagh, J. S. & Hedges, R. E. Variation of bone collagen amino acid delta13C values in archaeological humans and fauna with different dietary regimes: Developing frameworks of dietary discrimination. Am. J. Phys. Anthropol. 148, 495–511. https://doi.org/10.1002/ajpa.22065 (2012).Article 

    Google Scholar 
    Mora, A. et al. High-resolution palaeodietary reconstruction: Amino acid δ 13 C analysis of keratin from single hairs of mummified human individuals. Quatern. Int. 436, 96–113. https://doi.org/10.1016/j.quaint.2016.10.018 (2017).Article 

    Google Scholar 
    Matos, M. P. V., Konstantynova, K. I., Mohr, R. M. & Jackson, G. P. Analysis of the (13)C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources. Anal. Bioanal. Chem. 410, 7943–7954. https://doi.org/10.1007/s00216-018-1416-9 (2018).Article 
    CAS 

    Google Scholar 
    Bontempo, L. et al. Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes. Food Chem. 318, 126426. https://doi.org/10.1016/j.foodchem.2020.126426 (2020).Article 
    CAS 

    Google Scholar 
    Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
    CAS 

    Google Scholar 
    Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

    Google Scholar 
    Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).Article 

    Google Scholar 
    Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 

    Google Scholar 
    Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 

    Google Scholar 
    Hesse, T. et al. Insights into amino acid fractionation and incorporation by compound-specific carbon isotope analysis of three-spined sticklebacks. Sci. Rep. 12, 11690. https://doi.org/10.1038/s41598-022-15704-7 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Riekenberg, P. M. et al. Stable nitrogen isotope analysis of amino acids as a new tool to clarify complex parasite–host interactions within food webs. Oikos 130, 1650–1664. https://doi.org/10.1111/oik.08450 (2021).Article 
    CAS 

    Google Scholar 
    Carleton, S. A. & Del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812. https://doi.org/10.1111/j.1365-2435.2010.01700.x (2010).Article 

    Google Scholar 
    Perga, M. E. & Gerdeaux, D. ‘Are fish what they eat’ all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Grey, J. Trophic fractionation and the effects of diet switch on the carbon stable isotopic ‘signatures’ of pelagic consumers. SIL Proc. 1922–2010(27), 3187–3191. https://doi.org/10.1080/03680770.1998.11898266 (2000).Article 

    Google Scholar 
    Danfaer, A. Nutrient metabolism and utilization in the liver. Livest. Prod. Sci. 39, 115–127 (1994).Article 

    Google Scholar 
    Read, C. P. & Simmons, J. E. Biochemistry and physiology of tapeworms. Physiol. Rev. 43, 263–305 (1963).Article 
    CAS 

    Google Scholar 
    Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 1–9. https://doi.org/10.1186/s13071-017-2030-y (2017).Article 
    CAS 

    Google Scholar 
    Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).Article 
    CAS 

    Google Scholar 
    Gilbert, B. M. et al. You are how you eat: differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 119, 1393–1400. https://doi.org/10.1007/s00436-020-06619-1 (2020).Article 

    Google Scholar 
    Gilbert, B. M. et al. Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host—Parasite system from the Vaal River System, South Africa. Int. J. Parasitol. Parasites Wildl. 12, 134–141. https://doi.org/10.1016/j.ijppaw.2020.05.011 (2020).Article 

    Google Scholar 
    Felig, P. The glucose-alanine cycle. Metabolism 22, 179–207 (1973).Article 
    CAS 

    Google Scholar 
    Dale, R. A. Catabolism of threonine in mammals by coupling of L-threonine 3-dehydrogenase with 2-amino-3-oxobutyrate-CoA ligase. Biochem. Biophys. Acta. 544, 496–503 (1978).Article 
    CAS 

    Google Scholar 
    Jordan, P. M. & Akhtar, M. The mechanism of action of serine Transhydroxymethylase. Biochem. J. 116, 277–286 (1970).Article 
    CAS 

    Google Scholar 
    Linstead, D. J., Klein, R. A. & Cross, G. A. M. Threonine catabolism in Trypanosoma brucei. J. Gen. Microbiol. 101, 243–251 (1977).Article 
    CAS 

    Google Scholar 
    Hare, P. E., Fogel, M. L., Stafford, T. W. Jr., Mitchell, A. D. & Hoering, T. C. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292 (1991).Article 

    Google Scholar 
    Petzke, K. J., Boeing, H., Klaus, S. & Metges, C. C. Carbon and nitrogen stable isotopic composition of hair protein and amino acids can be used as biomarkers for animal-derived dietary protein intake in humans. J. Nutr. 135, 1515–1520 (2005).Article 
    CAS 

    Google Scholar 
    McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 

    Google Scholar 
    Fuller, B. T. & Petzke, K. J. The dietary protein paradox and threonine (15) N-depletion: Pyridoxal-5’-phosphate enzyme activity as a mechanism for the delta (15) N trophic level effect. Rapid Commun. Mass Spectrom. 31, 705–718. https://doi.org/10.1002/rcm.7835 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Bowyer, A. et al. Structure and function of the l-threonine dehydrogenase (TkTDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis. J. Struct. Biol. 168, 294–304. https://doi.org/10.1016/j.jsb.2009.07.011 (2009).Article 
    CAS 

    Google Scholar 
    Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: Reaction mechanism, physiological significance and hyperglycinemia. Proc. Jpn. Acad. https://doi.org/10.2183/pjab/84.246 (2008).Article 

    Google Scholar 
    Locasale, J. W. Serine, glycine and one-carbon units: Cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583. https://doi.org/10.1038/nrc3557 (2013).Article 
    CAS 

    Google Scholar 
    Kalhan, S. C. & Hanson, R. W. Resurgence of serine: An often neglected but indispensable amino Acid. J. Biol. Chem. 287, 19786–19791. https://doi.org/10.1074/jbc.R112.357194 (2012).Article 
    CAS 

    Google Scholar 
    Larsen, T., Wang, Y. V. & Wan, A. H. L. Tracing the Trophic fate of aquafeed macronutrients with carbon isotope ratios of amino acids. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.813961 (2022).Article 

    Google Scholar 
    Sweeting, C. J., Polunin, N. V. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601. https://doi.org/10.1002/rcm.2347 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Tarallo, A., Bailey, C., Agnisola, C. & D’Onofrio, G. A theoretical evaluation of the respiration rate partition in the Gasterosteus aculeatus-Schistocephalus solidus host-parasite system. Int. Aquat. Res. 13, 185. https://doi.org/10.22034/IAR.2021.1924974.1142 (2021).Article 

    Google Scholar 
    Takizawa, Y. et al. A new insight into isotopic fractionation associated with decarboxylation in organisms: Implications for amino acid isotope approaches in biogeoscience. Progress Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00364-w (2020).Article 

    Google Scholar 
    Ron-Harel, N. et al. T cell activation depends on extracellular alanine. Cell Rep. 28, 3011-3021.e4. https://doi.org/10.1016/j.celrep.2019.08.034 (2019).Article 
    CAS 

    Google Scholar 
    Wang, W. et al. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45, 463–477. https://doi.org/10.1007/s00726-013-1493-1 (2013).Article 
    CAS 

    Google Scholar 
    Mathis, D. & Shoelson, S. E. Immunometabolism: An emerging frontier. Nat. Rev. Immunol. 11, 81. https://doi.org/10.1038/nri2922 (2011).Article 
    CAS 

    Google Scholar 
    Guo, C. et al. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47, 664–673. https://doi.org/10.1016/j.fsi.2015.09.034 (2015).Article 
    CAS 

    Google Scholar 
    Peuss, R. et al. Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish. Nat. Ecol. Evol. 4, 1416–1430. https://doi.org/10.1038/s41559-020-1234-2 (2020).Article 

    Google Scholar 
    Smyth, J. D. Fertilization of Schistocephalus solidus in vitro. Exp. Parasitol. 3, 64–71 (1954).Article 
    CAS 

    Google Scholar 
    Schärer, L. & Wedekind, C. Lifetime reproductive output in a hermaphrodite cestode when reproducing alone or in pairs. Evol. Ecol. 13, 381–394 (1999).Article 

    Google Scholar 
    McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid delta13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Fry, B., Carter, J. F., Yamada, K., Yoshida, N. & Juchelka, D. Position-specific (13) C/(12) C analysis of amino acid carboxyl groups—Automated flow-injection-analysis based on reaction with ninhydrin. Rapid Commun. Mass Spectrom. https://doi.org/10.1002/rcm.8126 (2018).Article 

    Google Scholar 
    Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─A proof-of-concept study. Anal Chem 94, 2981–2987 (2022).Article 
    CAS 

    Google Scholar 
    Sun, Y. et al. A method for stable carbon isotope measurement of underivatized individual amino acids by multi-dimensional high-performance liquid chromatography and elemental analyzer/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 34, e8885. https://doi.org/10.1002/rcm.8885 (2020).Article 
    CAS 

    Google Scholar 
    Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Köster, D., Villalobos, I. M. S., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. New concepts for the determination of oxidation efficiencies in liquid chromatography-isotope ratio mass spectrometry. Anal. Chem. 91, 5067–5073. https://doi.org/10.1021/acs.analchem.8b05315 (2019).Article 
    CAS 

    Google Scholar 
    Boschker, H. T., Moerdijk-Poortvliet, T. C., van Breugel, P., Houtekamer, M. & Middelburg, J. J. A versatile method for stable carbon isotope analysis of carbohydrates by high-performance liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 22, 3902–3908. https://doi.org/10.1002/rcm.3804 (2008).Article 
    ADS 
    CAS 

    Google Scholar  More

  • in

    Bioclimatic atlas of the terrestrial Arctic

    Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).ADS 
    CAS 

    Google Scholar 
    Previdi, M., Smith, K. L. & Polvani, L. M. Arctic amplification of climate change: a review of underlying mechanisms. Environ. Res. Lett. 16, 093003 (2021).ADS 
    CAS 

    Google Scholar 
    Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3, 1–10 (2022).ADS 

    Google Scholar 
    Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).ADS 

    Google Scholar 
    Kopec, B. G., Feng, X., Michel, F. A. & Posmentier, E. S. Influence of sea ice on Arctic precipitation. Proc. Natl. Acad. Sci. 113, 46–51 (2016).ADS 
    CAS 

    Google Scholar 
    Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3, 10–23 (2022).ADS 

    Google Scholar 
    Overland, J. et al. The urgency of Arctic change. Polar Sci. 21, 6–13 (2019).ADS 

    Google Scholar 
    Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).ADS 
    CAS 

    Google Scholar 
    Ciavarella, A. et al. Prolonged Siberian heat of 2020 almost impossible without human influence. Clim. Change 166, 9 (2021).ADS 

    Google Scholar 
    Dobricic, S., Russo, S., Pozzoli, L., Wilson, J. & Vignati, E. Increasing occurrence of heat waves in the terrestrial Arctic. Environ. Res. Lett. 15, 024022 (2020).ADS 

    Google Scholar 
    Graham, R. M. et al. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 44, 6974–6983 (2017).ADS 

    Google Scholar 
    Knight, J. & Harrison, S. The impacts of climate change on terrestrial Earth surface systems. Nat. Clim. Change 3, 24–29 (2013).ADS 

    Google Scholar 
    Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).ADS 

    Google Scholar 
    Beck, P. S. A. et al. Changes in forest productivity across Alaska consistent with biome shift. Ecol. Lett. 14, 373–379 (2011).
    Google Scholar 
    Reichle, L. M., Epstein, H. E., Bhatt, U. S., Raynolds, M. K. & Walker, D. A. Spatial Heterogeneity of the Temporal Dynamics of Arctic Tundra Vegetation. Geophys. Res. Lett. 45, 9206–9215 (2018).ADS 

    Google Scholar 
    Sturm, M., Racine, C. & Tape, K. Increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).ADS 
    CAS 

    Google Scholar 
    Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).ADS 

    Google Scholar 
    Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).ADS 

    Google Scholar 
    Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).ADS 
    CAS 

    Google Scholar 
    Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
    Google Scholar 
    Virkkala, A.-M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Change Biol. 27, 4040–4059 (2021).CAS 

    Google Scholar 
    Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).ADS 

    Google Scholar 
    Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).ADS 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
    Google Scholar 
    Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Sci. Data 7, 248 (2020).
    Google Scholar 
    Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).
    Google Scholar 
    Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).ADS 

    Google Scholar 
    Slatyer, R. A., Umbers, K. D. L. & Arnold, P. A. Ecological responses to variation in seasonal snow cover. Conserv. Biol. 36, e13727 (2022).
    Google Scholar 
    Serreze, M. C. et al. Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts. Environ. Res. Lett. 16, 105009 (2021).ADS 

    Google Scholar 
    López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Change Biol. 27, 1704–1720 (2021).ADS 

    Google Scholar 
    Ennos, A. R. Wind as an ecological factor. Trends Ecol. Evol. 12, 108–111 (1997).CAS 

    Google Scholar 
    Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).ADS 

    Google Scholar 
    Boussetta, S. et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere 12, 723 (2021).ADS 
    CAS 

    Google Scholar 
    Munõz-Sabater, J. ERA5-Land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2019). Munõz-Sabater, J. ERA5-Land hourly data from 1950 to 1980. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).Hoyer, S. & Hamman, J. xarray: N-D labeled Arrays and Datasets in Python. J. Open Res. Softw. 5, 10 (2017).
    Google Scholar 
    Sen, P. K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).MATH 

    Google Scholar 
    Theil, H. A rank-invariant method of linear and polynomial regression analysis I, II and III. Indag. Math. 173 (1950).Hussain, M. M. & Mahmud, I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019).ADS 

    Google Scholar 
    Aalto, J. et al. High-resolution analysis of observed thermal growing season variability over northern Europe. Clim. Dyn. 58, 1477–1493 (2022).
    Google Scholar 
    Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 035004 (2018).ADS 

    Google Scholar 
    Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26 (2012).Wu, F., Jiang, Y., Wen, Y., Zhao, S. & Xu, H. Spatial synchrony in the start and end of the thermal growing season has different trends in the mid-high latitudes of the Northern Hemisphere. Environ. Res. Lett. 16, 124017 (2021).ADS 

    Google Scholar 
    Ruosteenoja, K., Räisänen, J., Venäläinen, A. & Kämäräinen, M. Projections for the duration and degree days of the thermal growing season in Europe derived from CMIP5 model output. Int. J. Climatol. 36, 3039–3055 (2016).
    Google Scholar 
    Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018).
    Google Scholar 
    McMaster, G. S. & Wilhelm, W. W. Growing degree-days: one equation, two interpretations. Agric. For. Meteorol. 87, 291–300 (1997).ADS 

    Google Scholar 
    Körner, C. Plant adaptation to cold climates. F1000Research 5, F1000 Faculty Rev-2769 (2016).Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Change 10, 1143–U134 (2020).ADS 

    Google Scholar 
    Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).ADS 

    Google Scholar 
    Mooney, P. A. & Li, L. Near future changes to rain-on-snow events in Norway. Environ. Res. Lett. 16, 064039 (2021).ADS 

    Google Scholar 
    Preece, C., Callaghan, T. V. & Phoenix, G. K. Impacts of winter icing events on the growth, phenology and physiology of sub-arctic dwarf shrubs. Physiol. Plant. 146, 460–472 (2012).CAS 

    Google Scholar 
    Putkonen, J. & Roe, G. Rain-on-snow events impact soil temperatures and affect ungulate survival. Geophys. Res. Lett. 30, (2003).Treharne, R., Bjerke, J. W. & Tømmervik, H. & Phoenix, G. K. Development of new metrics to assess and quantify climatic drivers of extreme event driven Arctic browning. Remote Sens. Environ. 243, 111749 (2020).ADS 

    Google Scholar 
    Bokhorst, S. et al. Impacts of extreme winter warming events on plant physiology in a sub-Arctic heath community. Physiol. Plant. 140, 128–140 (2010).CAS 

    Google Scholar 
    Russo, S., Sillmann, J. & Fischer, E. M. Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ. Res. Lett. 10, 124003 (2015).ADS 

    Google Scholar 
    Alduchov, O. A. & Eskridge, R. E. Improved Magnus Form Approximation of Saturation Vapor Pressure. J. Appl. Meteorol. Climatol. 35, 601–609 (1996).ADS 

    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
    Google Scholar 
    Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
    CAS 

    Google Scholar 
    De Frenne, P. et al. Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27, 2279–2297 (2021).ADS 

    Google Scholar 
    Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).ADS 
    CAS 

    Google Scholar 
    Berner, L. T., Jantz, P., Tape, K. D. & Goetz, S. J. Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska. Environ. Res. Lett. 13, 035002 (2018).ADS 

    Google Scholar 
    Walker, D. A. et al. Phytomass, LAI, and NDVI in northern Alaska: Relationships to summer warmth, soil pH, plant functional types, and extrapolation to the circumpolar Arctic. J. Geophys. Res. Atmospheres 108, (2003).Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).
    Google Scholar 
    Peng, S. et al. Change in snow phenology and its potential feedback to temperature in the Northern Hemisphere over the last three decades. Environ. Res. Lett. 8, 014008 (2013).ADS 

    Google Scholar 
    Wheeler, J. A. et al. Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175, 219–229 (2014).ADS 
    CAS 

    Google Scholar 
    Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).ADS 

    Google Scholar 
    Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int. J. Biometeorol. 61, 349–361 (2017).ADS 

    Google Scholar 
    Kling, M. M. & Ackerly, D. D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Change 10, 868–875 (2020).ADS 

    Google Scholar 
    Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608, 546–551 (2022).ADS 
    CAS 

    Google Scholar 
    Nathan, R. et al. Mechanisms of long-distance dispersal of seeds by wind. Nature 418, 409–413 (2002).ADS 
    CAS 

    Google Scholar 
    Sakai, A. Mechanism of Desiccation Damage of Conifers Wintering in Soil-Frozen Areas. Ecology 51, 657–664 (1970).
    Google Scholar 
    Wilson, J. W. Notes on Wind and its Effects in Arctic-Alpine Vegetation. J. Ecol. 47, 415–427 (1959).
    Google Scholar 
    Rantanen, M. et al. Bioclimatic atlas of the terrestrial Arctic, figshare, https://doi.org/10.6084/m9.figshare.c.6216368 (2023).Räisänen, J. Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change. The Cryosphere 15, 1677–1696 (2021).ADS 

    Google Scholar 
    Xu, J., Ma, Z., Yan, S. & Peng, J. Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. J. Hydrol. 605, 127353 (2022).
    Google Scholar 
    Behrangi, A., Singh, A., Song, Y. & Panahi, M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations. Geophys. Res. Lett. 46, 11358–11366 (2019).ADS 

    Google Scholar 
    Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J. & Lawrimore, J. H. The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. J. Clim. 31, 9835–9854 (2018).ADS 

    Google Scholar 
    Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An Overview of the Global Historical Climatology Network-Daily Database. J. Atmospheric Ocean. Technol. 29, 897–910 (2012).ADS 

    Google Scholar 
    Atlaskin, E. & Vihma, T. Evaluation of NWP results for wintertime nocturnal boundary-layer temperatures over Europe and Finland. Q. J. R. Meteorol. Soc. 138, 1440–1451 (2012).ADS 

    Google Scholar 
    Lindsay, R., Wensnahan, M., Schweiger, A. & Zhang, J. Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic. J. Clim. 27, 2588–2606 (2014).ADS 

    Google Scholar 
    Wang, C., Graham, R. M., Wang, K., Gerland, S. & Granskog, M. A. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution. The Cryosphere 13, 1661–1679 (2019).ADS 

    Google Scholar 
    Wesslén, C. et al. The Arctic summer atmosphere: an evaluation of reanalyses using ASCOS data. Atmospheric Chem. Phys. 14, 2605–2624 (2014).ADS 

    Google Scholar  More

  • in

    Water masses shape pico-nano eukaryotic communities of the Weddell Sea

    Guillou, L. et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ. Microbiol. 10, 3349–3365 (2008).Article 
    CAS 

    Google Scholar 
    Massana, R. Eukaryotic picoplankton in surface oceans. Annu. Rev. Microbiol. 65, 91–110 (2011).Article 
    CAS 

    Google Scholar 
    Rocke, E., Pachiadaki, M. G., Cobban, A., Kujawinski, E. B. & Edgcomb, V. P. Protist community grazing on prokaryotic prey in deep ocean water masses. PLoS ONE 10, e0124505 (2015).Article 

    Google Scholar 
    de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).Article 

    Google Scholar 
    Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 
    CAS 

    Google Scholar 
    Cordier, T. et al. Patterns of eukaryotic diversity from the surface to the deep-ocean sediment. Sci. Adv. 8, https://doi.org/10.1126/sciadv.abj9309 (2022).Giner, C. R. et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 14, 437–449 (2020).Article 

    Google Scholar 
    Obiol, A. et al. A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Mol. Ecol. Resour. 20, 718–731 (2020).Article 
    CAS 

    Google Scholar 
    Pernice, M. C. et al. Large variability of bathypelagic microbial eukaryotic communities across the world’s oceans. ISME J. 10, 945–958 (2016).Article 

    Google Scholar 
    Santoferrara, L. et al. Perspectives from ten years of protist studies by high‐throughput metabarcoding. J. Eukaryot. Microbiol. 67, 612–622 (2020).Article 

    Google Scholar 
    Schoenle, A. et al. High and specific diversity of protists in the deep-sea basins dominated by diplonemids, kinetoplastids, ciliates and foraminiferans. Commun. Biol. 4, 1–10 (2021).Article 

    Google Scholar 
    Sommeria-Klein, G. et al. Global drivers of eukaryotic plankton biogeography in the sunlit ocean. Science 374, 594–599 (2021).Article 
    CAS 

    Google Scholar 
    Tremblay, J. É. et al. Global and regional drivers of nutrient supply, primary production and CO2 drawdown in the changing Arctic Ocean. Prog. Oceanogr. 139, 171–196 (2015).Article 

    Google Scholar 
    Zoccarato, L., Pallavicini, A., Cerino, F., Umani, S. F. & Celussi, M. Water mass dynamics shape Ross Sea protist communities in mesopelagic and bathypelagic layers. Prog. Oceanogr. 149, 16–26 (2016).Article 

    Google Scholar 
    Biggs, T. E. G., Huisman, J. & Brussaard, C. P. D. Viral lysis modifies seasonal phytoplankton dynamics and carbon flow in the Southern Ocean. ISME J. 15, 3615–3622 (2021).Article 
    CAS 

    Google Scholar 
    Clarke, L. J., Bestley, S., Bissett, A. & Deagle, B. E. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 13, 734–737 (2019).Article 
    CAS 

    Google Scholar 
    Gast, R. J., Fay, S. A. & Sanders, R. W. Mixotrophic activity and diversity of Antarctic marine protists in austral summer. Front. Mar. Sci. 5, 13 (2018).Article 

    Google Scholar 
    Grattepanche, J. D., Jeffrey, W. H., Gast, R. J. & Sanders, R. W. Diversity of microbial eukaryotes along the West Antarctic Peninsula in austral spring. Front. Microbiol. 13, 844856 (2022).Article 

    Google Scholar 
    Hamilton, M. et al. Spatiotemporal variations in Antarctic protistan communities highlight phytoplankton diversity and seasonal dominance by a novel cryptophyte lineage. mBio 12, e0297321 (2021).Article 

    Google Scholar 
    Lin, Y. et al. Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula. Nat. Commun. 12, 4948 (2021).Article 
    CAS 

    Google Scholar 
    Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. 12, 5483 (2021).Article 
    CAS 

    Google Scholar 
    Vernet, M. et al. The Weddell Gyre, Southern Ocean: present knowledge and future challenges. Rev. Geophysics 57, 623–708 (2019).Article 

    Google Scholar 
    Callahan, J. E. The structure and circulation of deep water in the Antarctic. Deep‐Sea Res. 19, 563–575 (1972).
    Google Scholar 
    Janout, M. A. et al. FRIS revisited in 2018: on the circulation and water masses at the Filchner and Ronne ice shelves in the southern Weddell Sea. J. Geophys. Res.: Oceans 126, e2021JC017269 (2021).Article 

    Google Scholar 
    Orsi, A. H., Smethie, W. M. & Bullister, J. L. On the total input of Antarctic waters to the deep ocean: a preliminary estimate from chlorofluorocarbon measurements. J. Geophys. Res. 107, 3122 (2002).Article 

    Google Scholar 
    Hoppema, M., Fahrbach, E. & Schröder, M. On the total carbon dioxide and oxygen signature of the circumpolar deep water in the Weddell Gyre. Oceanol. Acta 20, 783–798 (1997).CAS 

    Google Scholar 
    Karstensen, J. & Tomczak, M. Age determination of mixed water masses using CFC and oxygen data. J. Geophys. Res. 103, 18599–18609 (1998).Article 
    CAS 

    Google Scholar 
    De Cáceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).Article 

    Google Scholar 
    De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

    Google Scholar 
    Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymetrical approach. Ecol. Monogr. 67, 345–366 (1997).
    Google Scholar 
    Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).Article 

    Google Scholar 
    Celussi, M., Bergamasco, A., Cataletto, B., Umani, S. F. & Del Negro, P. Water masses bacterial community structure and microbial activities in the Ross Sea, Antarctica. Antarct. Sci. 22, 361–370 (2010).Article 

    Google Scholar 
    Galand, P. E., Potvin, M., Casamayor, E. O. & Lovejoy, C. Hydrography shapes bacterial biogeography of the deep Arctic Ocean. ISME J. 4, 564–576 (2010).Article 

    Google Scholar 
    Hamdan, L. J. Ocean currents shape the microbiome of Arctic marine sediments. ISME J. 7, 685–696 (2013).Article 
    CAS 

    Google Scholar 
    Wilkins, D., van Sebille, E., Rintoul, S. R., Lauro, F. M. & Cavicchioli, R. Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects. Nat. Commun. 4, 2457 (2013).Article 

    Google Scholar 
    Flegontova, O. et al. Extreme diversity of diplonemid eukaryotes in the ocean. Curr. Biol. 26, 3060–3065 (2016).Article 
    CAS 

    Google Scholar 
    Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).Article 
    CAS 

    Google Scholar 
    Jeong, H. J. et al. Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. Ocean Sci. 45, 65–91 (2010).Article 
    CAS 

    Google Scholar 
    Stoecker, D. K., Hansen, P. J., Caron, D. A. & Mitra, A. Mixotrophy in the marine Plankton. Ann. Rev. Mar. Sci. 9, 311–335 (2016).Article 

    Google Scholar 
    Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc. Natl Acad. Sci. USA 116, 11824–11832 (2019).Article 
    CAS 

    Google Scholar 
    Gutierrez-Rodriguez, A. et al. High contribution of Rhizaria (Radiolaria) to vertical export in the California Current Ecosystem revealed by DNA metabarcoding. ISME J. 13, 964–976 (2019).Article 
    CAS 

    Google Scholar 
    Lampitt, R. S., Salter, I. & Johns, D. Radiolaria: major exporters of organic carbon to the deep ocean. Glob. Biogeochem. Cycles 23, GB1010 (2009).Article 

    Google Scholar 
    Suzuki, N. & Not, F. In Marine Protists: Diversity and Dynamics 179–222 (Springer Japan, 2015).Decelle, J. et al. Diversity, ecology and biogeochemistry of cyst-forming Acantharia (Radiolaria) in the oceans. PLoS ONE 8, e53598 (2013).Article 
    CAS 

    Google Scholar 
    Tashyreva, D. et al. Diplonemids—a review on “new“ flagellates on the oceanic block. Protist 173, 125868 (2022).Article 
    CAS 

    Google Scholar 
    Flegontova, O. et al. Environmental determinants of the distribution of planktonic diplonemids and kinetoplastids in the oceans. Environ. Microbiol 22, 4014–4031 (2020).Article 
    CAS 

    Google Scholar 
    Xu, D. et al. Microbial eukaryote diversity and activity in the water column of the South China sea based on DNA and RNA high throughput sequencing. Front. Microbiol. 8, 1121 (2017).Article 

    Google Scholar 
    Bråte, J. et al. Radiolaria associated with large diversity of marine alveolates. Protist 163, 767–777 (2012).Article 

    Google Scholar 
    Strassert, J. F. H. et al. Single cell genomics of uncultured marine alveolates shows paraphyly of basal dinoflagellates. ISME J. 12, 304–308 (2017).Article 

    Google Scholar 
    Yabuki, A. & Tame, A. Phylogeny and reclassification of Hemistasia phaeocysticola (Scherffel) Elbrächter & Schnepf, 1996. J. Eukaryot. Microbiol. 62, 426–429 (2015).Article 

    Google Scholar 
    Larsen, J. & Patterson, J. Some flagellates (Protista) from tropical marine sediments. J. Nat. Hist. 24, 801–937 (1990).Article 

    Google Scholar 
    Prokopchuk, G. et al. Trophic flexibility of marine diplonemids – switching from osmotrophy to bacterivory. ISME J. 16, 1409–1419 (2022).Article 
    CAS 

    Google Scholar 
    Arístegui, J. & Gasol, J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).Article 

    Google Scholar 
    Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4, e6372 (2009).Article 

    Google Scholar 
    Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015).Article 

    Google Scholar 
    Kolisko, M. et al. EukRef-excavates: seven curated SSU ribosomal RNA gene databases. Database 2020, baaa080 (2020).
    Google Scholar 
    Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119 (2019).Article 

    Google Scholar 
    Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083 (2019).Article 
    CAS 

    Google Scholar  More