Philippa Kaur

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

Duke, N. C. et al. Mar. Freshw. Res. 68, 1816–1829 (2017).Article 

Google Scholar 
Li, W. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-01020-5 (2023).Article 

Google Scholar 
Potapov, P. et al. Ecol. Soc. 13, 51 (2008).Article 

Google Scholar 
Hancock, S. et al. Earth Space Sci. 6, 294–310 (2019).Article 

Google Scholar 
Wade, C. M. et al. Forests 11, 539 (2020).Article 

Google Scholar 
Abhilash, P. C. Land 10, 201 (2021).Article 

Google Scholar 
Biermann, F., Kanie, N. & Kim, R. E. Curr. Opin. Environ. Sustain. 26–27, 26–31 (2017).Article 

Google Scholar 
den Elzen, M. et al. Energy Policy 126, 238–250 (2019).Article 

Google Scholar 
Betts, M. G. et al. Nature 547, 441–444 (2017).Article 
CAS 

Google Scholar 
Watson, J. E. M. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0490-x (2018).Article 

Google Scholar  More

Mishra, A. K. & Singh, V. P. J. Hydrol. 391, 202–216 (2010).Article 

Google Scholar 
Jiao, W. et al. Nat. Commun. 12, 3777 (2021).Article 
CAS 

Google Scholar 
Gampe, D. et al. Nat. Clim. Change 11, 772–779 (2021).Article 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Schwalm, C. R. et al. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
Li, Y. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01584-2 (2023).Fourth National Climate Assessment: Volume II—Impacts, Risks, and Adaptation in the United States (US Global Change Research Program, 2018).Daryanto, S., Wang, L. & Jacinthe, P. A. PLoS ONE 11, e0156362 (2016).Article 

Google Scholar 
Jiao, W. et al. J. Geophys. Res. Biogeosci. 127, e2021JG006431 (2022).Augspurger, C. K. Oecologia 156, 281–286 (2008).Article 

Google Scholar 
Lian, X. et al. Nat. Commun. 12, 983 (2021).Article 
CAS 

Google Scholar 
Buermann, W. et al. Nature 562, 110–114 (2018).Article 
CAS 

Google Scholar 
Lian, X. et al. Sci. Adv. 6, eaax0255 (2020).Article 

Google Scholar 
Jiao, W., Wang, L. & McCabe, M. F. Rem. Sens. Environ. 256, 112313 (2021).Article 

Google Scholar  More

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

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

Study site descriptionThe study was carried out at the edge of an arid region in mature plantations dominated by P. halepensis, of age of 40-50 years (Pinus halepensis), and their adjacent non-forested ecosystems. The sites were distributed across a climatic gradient from arid and semi-arid to dry sub-humid (Fig. 1, Supplementary Table S1). Three selected paired sites included: (1) An arid site at Yatir forest (annual precipitation, P: 280 mm; aridity index, AI: 0.18; elevation: 650 m; light brown Rendzina soil, and forest density: 300 trees ha−1), where a permanent flux tower has been operating since the year 2000 (http://fluxnet.ornl.gov). Note that an AI of 0.2 marks the boundary between arid and semi-arid regions. Yatir, with AI = 0.18, is formally within an arid zone, but on the edge of a semi-arid zone. (2) An intermediate semi-arid site in Eshtaol forest (P: 480 mm; AI: 0.37; elevation: 350 m; light brown Rendzina soil, and forest density: 450 trees ha−1). (3) A dry-subhumid site in northern Israel at the Birya forest (P: 770 mm; AI: 0.64; elevation: 755 m; dark brown Terra-Rossa and Rendzina soil, and forest density: 600 trees ha−1). Non-forest ecosystems were sparse dwarf shrublands, dominated by Sarcopoterium spinosum in a patchy distribution with a wide variety of herbaceous species, mostly annuals, that grew in between the shrubs during winter to early spring, and then dried out. All non-forested sites had been subjected to livestock grazing (exposing soils). Finally, an additional site that was characterized as Oak-forest vegetation was added. The site was dominated by two oak species, Quercus calliprinos and Quercus ithaburensis (P: 540 mm; AI: 0.4; see previous publication for more details on the oak site53). All sites were under high solar radiation load, with an annual average of approximately 240 Wm−2 in the arid region and only 3% lower in the northern site in the dry-subhumid region (Table 1).Mobile laboratoryMeasurements were conducted on a campaign basis using a mobile lab with a flux measurement system at all sites except the Yatir forest, where the permanent flux tower was used (http://fluxnet.ornl.gov;54). Repeated campaigns of approximately two weeks at each site, along the seasonal cycle, were undertaken during 4 years of measurements, 2012–2015 (a total of 6-7 campaigns per site, evenly distributed between the seasons) the 4 years of measurements were found to be representative of previous 70 years of precipitation record (Supplementary Figure S1 and Table S2). The mobile lab was housed on a 12-ton 4 × 4 truck with a pneumatic mast with an eddy-covariance system and provided the facility for any auxiliary and related measurements. Non-radiative flux measurements were undertaken using an eddy-covariance system to quantify CO2, sensible heat (H), and latent heat (LE) fluxes using a 3D sonic anemometer (R3, Gill Instruments, Hampshire, UK) and an enclosed-path CO2/H2O infrared gas analyzer (IRGA; LI-7200, LI-COR). Non-radiative flux measurements were accompanied by meteorological sensors, including air temperature (Ta), relative humidity (RH), and pressure (Campbell Scientific Inc., Logan, UT, USA), radiation fluxes of net solar- and net long-wave radiation (SWnet and LWnet, respectively), and photosynthetic radiation sensors (Kipp & Zonen, Delft, Holland). Raw EC data and the data from the meteorological sensors were collected using a computer and a CR3000 logger (Campbell Sci., Logan, UT, USA), respectively. The EC system was positioned at the center of each field site with the location and height aimed at providing sufficient ‘fetch’ of relatively homogeneous terrains. For detailed information on the use of the mobile lab and the following data processing of short and long-term fluxes see previous publications29,55,56.Data processingMean 30-min fluxes (CO2, LE, and H) were computed using Eddy-pro 5.1.1 software (LiCor, Lincoln, Nebraska, USA). Quality control of the data included a spike removal procedure. A linear fit was used for filling short gaps (below three hours) of missing values due to technical failure. Information about background meteorological parameters, including P, Ta, RH, and global radiation (Rg), was collected from meteorological stations (standard met stations maintained by the Israel Meteorological Service, https://ims.gov.il/en). The data were obtained at half-hourly time resolution and for a continuous period of 15 years since 2000.Estimating continuous fluxes using the flux meteorological algorithmEstimation of the flux-based annual carbon and radiation budgets was undertaken using the short campaign measurements as a basis to produce a continuous, seasonal, annual, and inter-annual scale dataset of ecosystem fluxes (flux meteorological algorithm). The flux meteorological algorithm method was undertaken based on the relationships between measured fluxes (CO2, LE, H, SWnet, and LWnet) and meteorological parameters (Ta, RH, Rg, VPD, and transpiration deficit, a parameter that correlated well with soil moisture, see main text and supplementary material of previous publication29. A two-step multiple stepwise regression was established, first between the measured fluxes (H, LE, and the ecosystem net carbon exchange) and the meteorological parameters measured by the mobile lab devices, and then between the two meteorological datasets (i.e., the variables measured by the Israel meteorological stations) for the same measurement times. Annual fluxes were calculated for the combined dataset of all campaigns at each site using the following generic linear equation:$$y={{{{{rm{a}}}}}}+{Sigma }_{i}{b}_{i}{x}_{i}$$
(1)
where, y is the ecosystem flux of interest, the daily average for radiative fluxes (LWnet and SWnet), non-radiative fluxes (H and LE), and daily sum for net ecosystem exchange (NEE), a and ({b}_{i}) are parameters, and ({x}_{i}) is Ta, RH, Rg, vapor pressure deficit, or transpiration deficit. The meteorological variables (({x}_{i})) were selected by stepwise regression, with ({b}_{i}=0) when a specific ({x}_{i}) was excluded.Based on this methodology, ecosystem flux data were extrapolated to the previous 7–15 years (since 2000 in the dry-subhumid and arid sites, since 2004 in the semi-arid sites, and since 2008 in the Oak-forest site) using all the available continuous meteorological parameters from the meteorological stations associated with our field sites. The long-term annual sums and means of extrapolated ecosystem fluxes were averaged for multi-year means of each site for the period of available extrapolated data. In the first, previously published phase of this study29, the extrapolation method was extensively tested, including simulation experiments at the arid forest site, where continuous flux data from the 20 years old permanent flux tower were available. Five percent of the daily data were selected by bootstrap (with 20 repetitions), a stepwise regression was performed for this sample, and then, the prediction of fluxes using Eq. 1 above was performed for the entire observation period. In the current phase of the study additional flux measurements are included with the R2 coefficients for the additional measurements ranging between 0.4-0.9, see Supplementary Table S3.The aridity index of the Oak-forest was in between those of the semi-arid and dry-subhumid Pine-forests (0.4 compared to 0.37 and 0.67, respectively). Therefore, to compare the Oak-forest with Pine-forest and non-forest sites, the average results from the semi-arid and dry-subhumid paired sites were used.Radiative forcing and carbon equivalence equationsTo compare the changes in the carbon and radiation budgets caused by forestation, we adopted the approach of Myhre et al. 30, and used Eq. 2:$${{RF}}_{triangle C}=5.35{{{{mathrm{ln}}}}}left(1+frac{triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(2)
where land-use changes in radiative forcing (RFΔC) are calculated based on the CO2 reference concentration, C0 (400 ppm for the measured period of study), and ΔC, which is the change in atmospheric CO2 in ppm, with a constant radiative efficiency (RE) value of 5.35. Here, ΔC is calculated based on the annual net ecosystem productivity (NEP; positive carbon gain by the forest, which is identical to net ecosystem exchange (NEE), the negative carbon removal from the atmosphere) as the difference between forested and non-forested ecosystems (ΔNEP) multiplied by a unit conversion constant:$$triangle C={left[{overline{{NEP}}}_{F}-{overline{{NEP}}}_{{NF}}right]}_{[gC{m}^{-2}{y}^{-1}]}cdot k ,[{ppm}]$$
(3)
where, k is a unit conversion factor, from ppm to g C (k = 2.13 × 109), calculated as the ratio between the air molar mass (Ma = 28.95; g mol−1), to carbon molar mass (Mc = 12.0107; g mol−1), and total air mass (ma = 5.15 × 109; g).Etminan et al. 57 introduced an updated approach to calculate the RE as a co-dependent of the change in CO2 concentration and atmospheric N2O:$${RE}={a}_{1}{left(triangle Cright)}^{2}+{b}_{1}left|triangle Cright|+{c}_{1}bar{N}+5.36,left[W,{m}^{-2}right]$$
(4)
where, (triangle {{{{{rm{C}}}}}}) is the change in atmospheric CO2 in ppm resulting from the forestation, as calculated in Eq. 3, (bar{{{{{{rm{N}}}}}}}) is the atmospheric N2O concentration in ppb (323), and the coefficients a1, b1, and c1 are −2.4 × 10−7 Wm−2ppm−1, 7.2 × 10−4 Wm−2 ppm−1, and −2.1 × 10−4 Wm−2ppb−1, respectively.Combining Eqs. 2 and 4 with an airborne fraction of (zeta =0.44)58, we obtain Eq. 5:$${{RF}}_{triangle C}={RE}cdot {{{{mathrm{ln}}}}}left(1+frac{zeta cdot triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(5)
Next, the annual average radiative forcing due to differences in radiation flux was calculated as follows:$${{RF}}_{triangle R}=frac{triangle Rcdot {A}_{F}}{{A}_{E}},left[W,{m}^{-2}right]$$
(6)
where, ΔR is the difference between forest and non-forest reflected short-wave or emitted long-wave radiation (ΔSWnet and ΔLWnet, respectively), assuming that the atmospheric incoming solar and thermal radiation fluxes are identical for the two, normalized by the ratio of the forest area (({A}_{F})) to the Earth’s area (({A}_{E}=5.1times {10}^{14},{m}^{2})).As forest conversion mostly has a lower albedo, the number of years needed to balance (‘Break even time’) the warming effect of changes in radiation budget by the cooling effect of carbon sequestration is calculated by combining Eqs. 5 and 6:$$^prime{Break},{even},{time}^prime=frac{{{RF}}_{triangle alpha }}{{{RF}}_{triangle C}},[{{{{{rm{years}}}}}}]$$
(7)
The multiyear averages of NEP for each of the three paired sites (forest and non-forest) were then modeled over a forest life span of 80 years. This was done based on a logarithmic model, modified for dryland, which takes as an input the long-term averages of NEP ((overline{{NEP}})) as in Eq. 3:$${{NEP}}_{t}=overline{{NEP}}(1-{exp }^{bcdot t}),left[g{{{{{rm{C}}}}}},{{{{{{rm{m}}}}}}}^{-2}{{yr}}^{-1}right]$$
(8)
where annual carbon gain at time t (NEPt) is a function of the multiyear average carbon gain ((overline{{{{{{rm{NEP}}}}}}})), forest age (t), and growth rate (b). Parameter b is a constant (b = −0.17) and is calculated based on the global analysis of Besnard et al. 59, limiting the data to only dryland flux sites60. Note that this analysis indicates NEP reaching a steady state and was used here to describe the initial forest growth phase, while growth analyses indicate that carbon sequestration peaks after about 80 years, followed by a steep decline50. This is consistent with the time scale for forest carbon sequestration considered here.In contrast to the one-year differences presented in Table 1 (ΔNEP), the net sequestration potential ((triangle)SP) for each of the paired sites was calculated as the accumulated ecosystem ΔNEPt along with forest age ((triangle) is the difference between forest and non-forest sites):$$triangle {SP}=mathop{sum }limits_{t=0}^{{age}}{triangle {NEP}}_{t}/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(9)
The ΔSP growth model was compared with previously published data of long-term carbon stock changes in arid forests (i.e., cumulative NEP over 50 years since forest establishment, t = 50), demonstrating agreement within ± 10%27.For comparison with previous studies, the carbon emission equivalent of shortwave forcing (EESF) was calculated using an inverse version of Eqs. 5 and 6 based on Betts1:$${EESF}={C}_{0}left({e}^{frac{{{RF}}_{triangle R}}{zeta cdot {RE}}}-1right)cdot k/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(10)
where, C0 is the reference atmospheric CO2 concentration (400 ppm, the average atmospheric concentration for the past decade), RFΔR is the multiyear average change in radiative forcing as a result of the change in surface albedo (Eq. 6 W m−2), RE is the radiative efficiency (Eq. 4, W m−2), ζ is the airborne fraction (0.44 as in Eq. 5), and k is a conversion factor, from ppm to g C (2.13 × 109 as in Eq. 3). Equation 10 was also used to calculate the emission equivalent of longwave forcing (EELF) with the RFΔR as the multiyear average change in radiative forcing as a result of the change in net long-wave radiation (ΔLWnet).Finally, the net equivalent change in carbon stock due to both the cooling effect of carbon sequestration and the warming effect due to albedo change (net equivalent stock change; NESC), was calculated by a simple subtraction:$${NESC}=triangle {SP}-{EESF},left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(11)
A comparison of the ΔSP (Eq. 9), the EESF (Eq. 10), and NESC (Eq. 11) with the same metrics as those used in other studies1,14,61 was done when appropriate. An exception was made for Arora & Montenegro (2011), where only carbon stock changes (ΔSP) were available in carbon units, and biogeophysical (BGP) and biogeochemical (BGC) effects were expressed as temperature changes. To overcome this metric difference, we converted the biogeophysical to carbon equivalent units (EESF + EELF) by multiplying the carbon stock changes (ΔSP) by the ratio between the BGP and BGC effects on temperature (EESF + EELF = ΔSP × BGP/BGC).Statistical and data analysesThe paired t-test was used to compare multi-annual averages of all variables between forested and adjacent non-forested sites and between sites across the climatic gradient. The variables of interest that were detected for their significant differences were albedo, net radiation and its longwave and shortwave components, latent heat fluxes, sensible heat fluxes, and net ecosystem productivity. All statistical and data analyses were performed using R 3.6.0 (R Core Team, 2020)62.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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

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