Aerobic methane-oxidizing bacteria (MOB) substantially reduce methane fluxes from freshwater sediments to the atmosphere. Their metalloenzyme methane monooxygenase (MMO) catalyses the first oxidation step converting methane to methanol. Its most prevalent form is the copper-dependent particulate pMMO, however, some MOB are also able to express the iron-containing, soluble sMMO under conditions of copper scarcity. So far, the link between copper availability in different forms and biological methane consumption in freshwater systems is poorly understood. Here, we present high-resolution profiles of MOB abundance and pMMO and sMMO functional genes in relation to copper, methane and oxygen profiles across the oxic-anoxic boundary of a stratified lake. We show that even at low nanomolar copper concentrations, MOB species containing the gene for pMMO expression are present at high abundance. The findings highlight the importance of copper as a micronutrient for MOB species and the potential usage of copper acquisition strategies, even under conditions of abundant iron, and shed light on the spatial distribution of these microorganisms. Alpha- and Gammaproteobacteria (α-MOB and γ-MOB) and the Verrucomicrobia. They efficiently mitigate the emission of methane (CH4) generated in freshwater systems while utilizing CH4 as their sole carbon and energy source1. The enzyme methane monooxygenase (MMO) plays a key role for this process by catalysing the first oxidation step, the conversion of CH4 to methanol under ambient conditions. Two forms have been described, the soluble MMO (sMMO) and the membrane-bound, particulate MMO (pMMO)2. Whereas most known MOB express pMMO, sMMO production has been characterized in only a few organisms3. The conserved gene segments mmoX and pmoA encode subunits of sMMO and pMMO, respectively, and serve as biological markers to track MOB in environmental samples3. sMMO has a well-characterized di-iron catalytic centre2, but the atomic structure of the pMMO active site is still a matter of debate. Several competing models with different metals and different numbers of metal atoms at the active site of pMMO have been proposed6. However, it is generally agreed that pMMO is a copper-dependent enzyme. Copper (Cu) has a regulatory effect on MOB activity, especially on the biosynthesis of the pMMO and sMMO enzymes and switching between these in cells able to express both7. According to experiments with axenic cultures, sMMO expression occurs under low Cu to biomass levels (<1 µM), whereas pMMO is predominant at concentrations above 5 µM8. Nevertheless, in cells grown under sMMO expressing conditions, low but detectable levels of pMMO transcription have been measured10. Well-defined incubation experiments with environmental samples documented the influence of Cu on the MOB community structure and abundance and composition of functional gene transcripts12. Cu addition stimulated pmoA gene transcription and promoted growth of MOB, which mostly lacked mmoX. Some MOB utilize special mechanisms to regulate their Cu homeostasis, also in response to Cu toxicity at high levels. The chalkophore methanobactin, the extracellular component of a Cu acquisition system, binds Cu with high affinity and specificity and is able to increase the bioavailable Cu fraction by dissolving Cu from soluble, mineral, and humic sources, but open questions about its role still remain14. It has recently been proposed that methanobactin works in concert with a protein called MmoD to modulate the Cu-switch of sMMO and pMMO15, however, other proteins are also involved in the Cu or Cu-methanobactin uptake and/or the Cu-switch in MOB20.Aerobic methane-oxidizing bacteria (MOB) are phylogenetically diverse and mainly group among the in-situ conditions in aquatic systems. Many studies have assessed the role of growth substrates and physical parameters on MOB activity22, but it remains unclear if bioavailable Cu limits the distribution of MOB and affects the dominant enzymatic pathways of CH4 oxidation in natural systems23. We make a first attempt to reduce this uncertainty by combining trace metal speciation measurements with marker-gene based analysis of MOB in the water column of a stratified lake. We explore the hypothetical link between the depth distribution patterns of different Cu fractions and the abundance of MOB and their functional genes.The hypothesis that Cu acts as a controlling variable for the distribution of MOB with different enzymatic pathways has not yet been tested under 4 and Cu dynamics in-situ, we studied seasonally stratified Rotsee, a small freshwater lake (1 km2) with pronounced sedimentary CH4 production and aerobic CH4 oxidation in its water column24. We conducted four field campaigns at the early stage (June 2013), peak (August 2013), and end of stratification before the lake overturns (September 2014 and September 2015). CH4 concentrations were highest close to its production site in the sediment (270–710 µM) and steadily decreased towards the oxycline where CH4 was predominantly consumed  detected in the oxic epilimnion during all campaigns indicated that CH4 removal within the water column of Rotsee was highly efficient. Nevertheless, surface water was still oversaturated in CH4 relative to the atmosphere and thus Rotsee was a source of CH4. For all campaigns, we found that oxygen (O2) concentrations dropped from ~400 µM at the surface to below detection limit (<20 nM) at the oxycline, which varied in depth between 6 m to 9 m across sampling dates 27. Further, isotopic CH4 values also showed slight increases in the anoxic, dark hypolimnion of Rotsee, which implies that some CH4 is already oxidized at these depths. O2 was completely consumed in the upper part of the specified zones, except for June 2013 where O2 and CH4 gradients did not show any overlap and thus no O2 was measured in the potential CH4 oxidation zone.We focused our field sampling on depths with the greatest potential for active CH4 oxidation, several metal fractions were quantified throughout the water column. We determined bioavailable metals by deploying diffusive gradients in thin-film (DGT) samplers at 0.25–1 m resolution. The DGT technique is based on the diffusion characteristics of different metal chemical species28. The samplers allow low molecular weight compounds such as simple inorganic and labile organic complexes as well as free metal ions to diffuse across a diffusive layer to be sorbed at an ion exchange layer. This accumulation process mimics uptake of dissolved metals by organisms. Previous studies showed that the DGT measurement is a good indicator for in-situ metal bioavailability29. However, the results might underestimate bioavailable Cu as DGT provides a time-integrated concentration and Cu may be bound to large complexes with low diffusion coefficients. Some MOB possess specific uptake pathways such as the ligand methanobactin14, which may increase the bioavailable Cu fraction.To assess the biogeochemical role of Cu on CHDGT) concentrations were usually found within the oxic zone of Rotsee , which reached concentrations in the low nM range  profiles were quite homogeneous throughout the water column and do not elucidate any Cu-binding capacity therein  or enlarge this fraction by the use of different Cu uptake mechanisms18. These auxiliary peptides collect Cu and bind it with high affinity, hence, they are not included in the CuDGT fraction, and only measured as CuDiss. We monitored strong CuDGT and CuDiss gradients towards the CH4 oxidation zones where concentrations were depleted  showed maximum values within the CH4 oxidation zones  distributions in Rotsee down to 0.5 m resolution. 16S rRNA sequencing yielded a total number of 3679 Operational Taxonomic Units (OTUs). In-depth analysis of the OTU data set revealed a total of 15 OTUs belonging to γ-MOB and one α-MOB OTU  while the remaining two belonged to a family known not to contain any MMO (LD19), which hence probably lack the ability of oxidizing CH432. As the applied primer pair for pmoA detection did not cover verrucomicrobial pmoA sequences, and genomic investigations of freshwater Verrucomicrobia implicated them as (poly)saccharide degraders33, the verrucomicrobial OTUs were not included in the further analysis of the data.To link physico-chemical conditions with microbial CH4 oxidation process in Rotsee  directly below the oxycline in August 2013 and September 2014  were found in deeper layers where neither O2 nor oxygenic phototrophs were detected 38. Or, they could be involved in anaerobic oxidation of CH4 (AOM) and use O2 or other electron acceptors provided by different reaction pathways40. Anaerobic methanotrophic archaea could also play a role in oxidizing CH4 at depths depleted in O241, however, in this study DNA was only screened for bacteria. MOB distribution in September 2015 showed an abundance maximum above the oxycline with a majority of α-MOB  yielded pmoA copy numbers between 104–107 copies L−1 , 0.98 (August 2013), 0.87 (September 2014) and 0.40 (September 2015). However, absolute numbers suggested by pmoA analysis were on average a factor of ~30 lower than those derived from 16S rRNA gene analysis, possibly a result of methodical biases. Correspondingly, the calculated proportion of cells containing copies of pmoA was between 0.002–0.2% . We propose that sMMO-mediated CH4 oxidation is of minor importance in Rotsee at the investigated sampling dates, although additional studies are needed to confirm this.Relative abundances of les Fig. . This in014 Fig. , and thi4 oxidation zones of August 2013 and September 2014, the concentrations of pmoA genes rapidly increased at the upper boundary of the zones in which they showed highest abundances , however, R2-values for the other three campaigns were much lower  indicating that MOB did not constitute the main component of the CuPart concentrations.Having a closer look on the specific CHces Fig. . Their cart Fig. . In Sept4 oxidation zones from measured concentration gradients and a site-specific coefficient for turbulent diffusion , to about tenfold higher ones in the other three campaigns (27.6–42.9 mmol m−2 d−1). These O2 flux estimates do not account for potential oxygenic photosynthesis within the seemingly anoxic CH4 oxidation zone25. Theoretical CH4 fluxes out of the sediment were about half to one order of magnitude lower than for O2 (2.7–13.3 mmol m−2 d−1), except for June 2013 (10.2 mmol m−2 d−1), which indicates O2 is being consumed by additional processes, like mineralization and oxidation of other reduced substances. Dissolved and bioavailable Cu fluxes from the lake’s surface down to where they are depleted were in the nmol m−2 d−1 range, with values for CuDiss being about ten times higher than for CuDGT  for a single MOB cell. The Redfield ratio stands for the specific elemental composition (C:N:P) of a cell reflecting the conditions under which it grows46. Assuming an average MOB spherical cell diameter of 2 μm, the biovolume of a single cell (μm3 cell−1) can be converted into biomass (mol carbon cell−1) by applying a carbon (C) conversion factor of 6.4 fmol C μm−3 26. This results in a C content of 0.03 pmol C cell−1. Considering the studies focusing on the type and number of Cu ions in pMMO’s metal centre, we choose a range of 2–15 Cu ions per pMMO4. Accepting the estimate of Nihous by which cell membrane walls are able to bind between 1000–4000 pMMO enzymes47, a single MOB organism contains between 2000–60′000 Cu atoms in pMMO, which amounts to a mean of 4 × 10−20 mol Cu cell−1. Putting this value in relation to the calculated C content, we obtained an averaged proportion for Cu:C of 10−6. This ratio is more likely to be an under- than an overestimate since MOB cells contain Cu acquisition and storage proteins and use Cu also for other enzymes besides pMMO23. Nevertheless, based on these cellular Cu contents we calculated a contribution of MOB to CuPart that underestimates the actually measured in-situ CuPart concentrations by a factor of 102–104 . With the exception of June 2013, CuPart concentrations showed distinct peaks within the defined CH4 oxidation zones, which coincided well with chlorophyll a (Chl-a) maxima and turbidity (Turb) measurements 25, we suggest that the difference in Cu content between MOB and phytoplankton explains most of the observed discrepancy between measured CuPart and the calculated CuPart contribution of MOB within the defined zones  and the nitrous oxide reductase (Nos), are Cu-rich proteins23. Nitrite (NO2-) and nitrate (NO3-) concentrations in Rotsee were low (max. 30 µM) and it seems that the organisms containing these proteins potentially only make a small contribution to CuPart , belongs to the Cu-containing membrane-bound monooxygenase (CuMMO) superfamily and catalyses the initial ammonia (NH3) oxidation step54. Inferred sites of NH4+ oxidation or uptake by primary producers in Rotsee only weakly overlapped with CuPart maxima  and manganese (MnPart) did not appear to be strongly related to the CuPart profiles  is continuously consumed by SOB during the day during which sampling campaigns were conducted56 and thus resulting CuPart precipitation with STot would be minimal.Alternative mechanisms for Cuide Fig. 55, but w4 oxidation in lakes and specifically for Cu-dependent MOB activity. During stratification, MOB in Rotsee were abundant within a zone with CH4 supply from the sediment and O2 release from oxygenic photosynthesis  is secreted into the environment to collect Cu16. MopE* shares sequence resemblance to the CorA (copper repressible) protein. CorA is located on the cell surface and it is postulated to be involved in the uptake of Cu into the cell17. A copper resistance protein-mediated (CopCD) Cu uptake may play a role for delivering Cu to the cytosol of the cell19. Additional insight in how MOB manage their Cu demand has also been provided by the recent discovery of a new family of Cu storage proteins (Csp)20. Such mechanisms are likely to be crucial for MOB ecology in low-Cu environments such as Rotsee.Our findings have important implications for CHsis Fig. . Most ofnes Fig. . Howeverin-situ study of Cu and CH4 oxidation reveals that MOB species containing the gene for a potential Cu-dependent enzymatic pathway are abundant despite low bioavailable Cu concentrations and excess of Fe. We provide evidence that other biological processes and abiotic reactions could have a profound impact on the availability of Cu in the water column, which may in turn have consequences for the ecology of CH4 oxidation, a critical process in the global carbon cycle. Since Rotsee is representative for mid-latitude, nutrient-rich lakes in terms of topography and chemical cycling, we expect that the observed distribution of Cu species as well as the vertical zonation of MOB and phytoplankton is typical for such lakes. Therefore, the proposed Cu competition and inferred mechanisms of MOB adaptation to Cu scarcity may be common in numerous other lakes.This detailed 2), eutrophic subalpine lake in Switzerland with a maximum depth of 16 m. Rotsee exhibits a stable summer stratification from approximately April-November with an oxycline between 6 m and 9 m depth25. Large amounts of CH4 are released from the sediments reaching concentrations up to 1 mM before winter overturn24.Rotsee is a small 57 was used for high resolution profiling and sampling: conductivity, turbidity, depth, temperature and pH were monitored with a CTD multi-parameter probe and Chl-a with an ECO-FL fluorescence probe (Wetlabs). Profiles of dissolved oxygen (O2) concentrations were obtained from two needle-type optodes  with detection limits of 125 nM  and 20 nM (trace), respectively. Photosynthetically active radiation (PAR) was recorded with a spherical quantum sensor . Detection limit of PAR sensing photon flux was 0.1 µmol m−2 s−1. Water for chemical analysis was taken using an integrated rosette syringe sampler (12 × 60 ml syringes), which could be triggered remotely during profiling. Sampling was carried out across the whole water column with high resolution in the oxycline (0.25–0.5 m) and in one-meter steps otherwise. Aliquots for sulphate (SO42−), nitrite (NO2−), nitrate (NO3−) and ammonium (NH4+) were filtered (0.22 μm cellulose acetate syringe filters) and analysed on the same day by ion chromatography  and flow-injection analysis . Samples for total sulphide (STot) detection were immediately fixed with zinc acetate  and determined spectrophotometrically58. Equipment for metal sampling and filtering was acid-washed and rinsed with nanopure water before use. Triplicate samples for dissolved (<0.45 μm cellulose acetate syringe filters) and total metals  were acidified on-site to a final concentration of 0.1 M HNO3 and analysed with inductively coupled plasma mass spectrometry . Particulate metal species  were calculated as CuPart = CuTot − CuDiss. CuPart errors were estimated by standard deviation propagation from dissolved and total metal measurements. Aliquots for dissolved organic carbon (DOC) analysis were filtered  into pre-combusted glass vials and acidified with 2 M HCl  and measured on a total carbon analyser  equipped with a non-dispersive infrared detector. Outliers of triplicate samples were removed applying Grubbs’ outlier test. Methane (CH4) samples were collected with a Niskin bottle or via pumping with a gas tight tubing  attached to PIA. 120 ml serum bottles were filled anoxically, poisoned with NaOH (pH > 12) or Cu(I)Cl, and sealed with butyl-rubber stoppers and aluminium crimps. In the laboratory, a 20 ml nitrogen (N2) headspace was inserted. After overnight equilibration at room temperature, CH4 was measured by headspace injection using a gas chromatograph  equipped with a Carboxen 1010 column  and a flame ionization detector (detection limit: ~10 nM). Vertical diffusive fluxes of dissolved compounds into the CH4 oxidation zones  near the deepest part of Rotsee . A custom built device , agarose cross-linker  and nanopure water were used to generate diffusive and resin hydrogels according to Odzak et al.30. To initiate chemical polymerisation freshly mixed ammonium persulfate  and TEMED  were added. The resin hydrogel contained additional ion-exchange resin . The gels were hydrated and cleaned with nanopure water and stored in 0.01 M NaNO3. Each piston was loaded with a resin gel, a diffusive gel (0.8 mm thickness), a protective filter  and a plastic cap , bioavailable trace metal concentrations  were analysed via ICP-MS. The accumulated mass of the analytes was calculated following Davison28 with published diffusion coefficients . The negligible thickness of the diffusive boundary layer was disregarded. Significant outliers were determined based on Grubbs’ outlier test.DGT preparations were performed in a clean room (except for June 2013). All plastic devices, containers and membrane filters were soaked in diluted HNO2 and stored at −80 °C until DNA was extracted using a PowerWater® DNA Isolation Kit (MoBio Laboratories). Extracted DNA was quantified using a NanoDrop 1000 Spectrophotometer (Thermo Fisher).Water samples were pre-filtered (<5.0 µm) and subsequently filtered onto 0.2 µm polycarbonate membranes. Filters were packed into plastic bags, immediately frozen in liquid N59. 16S rRNA gene PCRs, library preparation and sequencing were conducted by Microsynth. Sequence data was analysed by the Genomic Diversity Centre (ETH Zurich), which clustered the sequences into operational taxonomic units (OTUs) with a cut-off value of 97% similarity using the Uparse workflow with usearch (v8.1.1812_i86linux64). Taxonomic identity was classified via UTAX based on the GreenGene database . Narrowing the data set gave a final alignment of 1 α-MOB, 15 γ-MOB, and 5 potential verrucomicrobial MOB (Supplementary Table https://blast.ncbi.nlm.nih.gov/Blast.cgi). OTUs of sulphur oxidizing bacteria (SOB) were chosen based on the assignments to phylum Chlorobi and orders Chromatiales and Legionellales, and were checked with literature60 (Supplementary txt-file “16S rRNA sequences_SOB”).Illumina MiSeq sequencing technology was performed on amplicons obtained with bacterial primers 341f (5′-CCTACGGGNGGCWGCAG-3′) and 805r (5′-GACTACHVGGGTATCTAATCC-3′)E = 10slope−1/). Product lengths were additionally verified by gel electrophoresis (1.5% agarose gel). All samples were run on a Roche Light Cycler 480 (Roche Diagnostics).For analysing 16S rRNA and methane monooxygenase (MMO) functional genes, the limit of detection (LOD) was set as the highest crossing point  determined in PCR or extraction blanks. A sample was considered not detectable if either its Cp-value was ≥ Cp-value LOD or if no clear, or multiple, melting temperature (Tm) peak(s) were detected in comparison to the positive control. The limit of quantification (LOQ) was the concentration of the lowest quantifiable standard dilution with a standard deviation of quadruplicate Cp-values < 0.5. Samples above LOD were described not quantifiable when Cp-values of replicates differed more than 0.5, Cp-values were > LOQ, or when 2 out of 3 replicates were < LOD. However, some sample concentrations were estimated using standard curve extrapolation below LOQ. qPCR efficiency was calculated from the slope of the standard curve . Copy numbers of pmoA were measured using an adjusted protocol from Henneberger et al.63 (Supplementary Table mmoX were performed following the conditions listed in Supplementary Table 7−5 × 100 copies per reaction). All standards were run in quadruplicates, all samples in triplicates. Genomic DNA of several axenic culture strains served as positive and negative controls . Normality of the data was tested by the Shapiro-Wilk test. Differences between the three zones were evaluated using the Kruskal-Wallis test followed by the Mann-Whitney pairwise test if normality was not met. Else, a one-way ANOVA following a Tukey’s HSD test was performed. p-values < 0.05 were considered significant. Tests were conducted for all seasons, and for single seasons each  to apply statistical testing using the Past3.18 statistic software .Supplementary InformationDataset 1Dataset 2


The design and assembly of monodisperse colloidal particles not only advances the development of functional materials, but also provides colloidal model systems for understanding phase behaviors of molecules. This communication describes the gram-scale synthesis of highly uniform colloidal cuboids with tunable dimension and shape biaxiality and their molecular mesogen-like assembly into various mesophasic structures in pristine purity. The synthesis relies on the nanoemulsion-guided generation of ammonium sulfate crystals that template the subsequent silica coating. The shape of the cuboidal particles can be tuned from square platelike, to biaxial boardlike, and to rodlike by independently controlling the length, width and thickness of the particles. We demonstrated the assembly of the cuboidal colloids into highly pure mesoscopic liquid crystal phases, including smectic A, biaxial smectic A, crystal B, discotic, and columnar phases, as well as established a correlation between mesophasic formation and colloidal biaxiality in experiments. Monodisperse biaxial colloidal particles are used as model systems to understand the phase behavior of biaxial molecules but high-quality biaxial colloids are limited. Here the authors describe gram-scale synthesis of uniform biaxial colloidal cuboids and their assembly into mesophasic structures. The nature and transition of these mesophasic structures are strongly dependent on the geometry and uniformity of colloidal particles8. Because of their fluidity and mesoscopic ordering in different forms, colloid-based liquid crystalline materials show great potential in applications, ranging from sensors to display devices and to metamaterials10. Compared with molecular LC systems, LC phases of colloidal particles exhibit distinctive features and they are (i) all lyotropic; (ii) driven by entropy alone; (iii) susceptible to polydispersity of constituent colloids; and (iv) inexpensive, thermally stable, and more susceptible to external fields11. Moreover, they can be quantitatively studied on the single particle level owing to the unique dimension  of colloids. Thus, it is of great fundamental and technological importance in designing novel colloidal particles and understanding their LC behaviors15.Liquid crystalline materials are ubiquitous in nature and our everyday life. Other than molecular liquid crystals (LCs), suspensions of some shaped colloidal particles  are known to exhibit entropy-driven liquid crystalline phases, such as nematic, smectic, bowlic, discotic 17, ions 20, surfactants 21, polymers 22, and biomolecules 23. Biaxial colloidal particles are of great interest, due to their ability to form biaxial phases with two optic axes , which are promising candidates for LC displays25. Shape biaxiality of biaxial colloids  can promote the formation of the biaxial ordering. In spite of the fact that biaxial LC molecules represent a large class of mesogens for materials discovery24, experimental studies of biaxial colloidal systems have been rare27. This is largely due to the limited access to high-quality colloids with the inherent biaxial shape. Currently available biaxial colloids include bent silica rod28, goethite29, hectorite30, and lead carbonate boardlike particles31 with unique D2h symmetry34, of which goethite particles are mostly studied. These goethite colloids, however, show very broad polydispersity (up to 55%) in size and shape. The assembly of such colloids leads to the coexistence of multiple phases and genetic defects (rather than single pristine phases), which hinders a deep understanding of their LC behaviors and the discovery of potential applications of resulting materials35.The past decades have witnessed rapid advances in the synthesis of anisotropic colloidal particles with controlled shapes and crystallographic facets by using structure-directing agents4)2SO4) crystals that template the subsequent hydrolysis of tetraethoxysilane (TEOS) to produce uniform cuboids in a one-pot reaction with iron (III) cation (Fe3+), PVP, and sodium citrate (SC) as structure-directing agents. The resulting cuboidal particles exhibit a narrow size distribution (polydispersities <5%) in all three dimensions. The shape of the particles can be tuned from square platelike, to biaxial boardlike, and to rodlike by independently controlling the length (L), width (W), and thickness (T) of the particles. These colloidal cuboids resemble organic boardlike mesogens to assemble into a rich variety of LC phases, including uniaxial smectic A (SmAu), biaxial smectic A (SmAb), crystal B, N−, and Col phases. The formation of pristine, pure mesophases (rather than coexistence of multi-phases in one system) enables us to experimentally reveal the critical role of shape biaxiality in cuboidal colloid assembly. A phase diagram is constructed to identify the stable phases and describe the phase transition of the assembly system. This work provides a class of colloidal building blocks for fabricating biaxial structural materials with potential applications in fast electro-optical switching devices. Moreover, it offers a mesoscopic model of LC molecules for understanding molecular assembly.This communication presents the gram-scale synthesis of highly monodisperse colloidal cuboids with tunable dimension and shape biaxiality and their molecular mesogen-like assembly into various mesophasic structures in pristine purity. This synthetic method relies on the nanoemulsion-guided formation of well-defined ammonium sulfate ((NH4)2SO4 crystals initiated within discrete nanoscale reactors of water emulsions containing directing agents, Fe3+, PVP, and SC, and subsequent coating of silica on the surface of crystal templates. In a typical synthesis, an aqueous solution containing ethanol, iron (III) sulfate (Fe2(SO4)3), and SC was first emulsified in n-pentanol dissolved with PVP to generate nanosized water-in-oil emulsions  was then introduced into the mixture, followed by or together with the addition of silica precursor, TEOS. The rapid reaction (in ~30–60 s) of NH3 with Fe2(SO4)3 within the emulsion droplets produced highly uniform (NH4)2SO4 crystals with well-defined boardlike shape. Dynamic light scattering (DLS) analysis shows that the emulsion droplets were around 160 ± 6.4 nm in diameter 2SO4 crystals is attributed to the high uniformity of the emulsions that serve as nanoreactors and determine the amount of salt precursors available for individual crystals. The subsequent hydrolysis and condensation of TEOS—which was catalyzed by residue of NH3—on the surface of crystals yielded monodisperse cuboidal colloids  composed of (NH4)2SO4 core and silica shell 2SO4 salt was directly added in the reaction without in situ generation of (NH4)2SO4 or when the synthesis was conducted in bulk water instead of water nanoemulsions 2SO4 cores can be readily removed by washing the particles with water, leaving hollow silica shells with boardlike shape 2SO4 crystals. The angles between two adjacent facets  of the hollow shell were found to be ~54° and ~61°, respectively.It is noted that the cuboidal colloids preserve the shape and even crystal facets of crystal templates. The 2SO4 and silica components in particles 2SO4 crystal was further confirmed by powder X-ray diffraction (XRD) pattern 2SO4. It is worth noting that the peak intensity ratios of the (020)/(111) and (040)/(111) are significantly higher than those of bulk crystals, indicating that the crystalline cores of cuboids are abundant with {010} facets. Moreover, the symmetry of TEM electron diffraction pattern further confirmed that the core of particle is a single crystal bounded mainly by {010} planes, which is consistent with the XRD results  image and energy-dispersive X-ray spectrometer (EDS) maps of a typical particle indicate a uniform distribution of N, S, C, Si, and O, but no presence of Fe across the whole particle Fig. . Fourierern Fig. . The diflts Fig. .Fig. 2Ch3, Fe2(SO4)3, SC, ethanol, and TEOS  could be obtained by post-coating of silica on the particles, while not losing their boardlike shape.The morphology and dimensions of cuboidal colloids can be controlled by varying reaction conditions, such as molecular weight (MW) of PVP, reaction temperature, and the amount of NHEOS Fig. –14. The  μl Fig. . When thf L Fig. . When etunt Fig. . Furtherunt Fig. . On the ike Fig. . When exike Fig. 36. Both PVP Fig. . Moreove3+ cation, PVP, and SC were found to be indispensable for the formation of colloidal cuboids with a defined shape. The presence of Fe3+ cation was found to be critical for the formation of shaped (NH4)2SO4 crystals. Other than Fe3+, Fe2+ and Ce3+ cations could also lead the formation of shaped crystals, thanks to their preferential strong interaction with the {010} crystal facet relative to others 2SO4, probably due to the low valence and thereby weak interaction with crystal surfaces20. Moreover, both PVP and SC are also known as structure-directing agents that can selectively interact with various crystallographic planes39. Take noble metal nanocrystals as an example, PVP binds preferentially to {100} facets, while SC binds more strongly to {111} than {100} facets. Given the fact that rectangle cuboidal shape can be generated only with the presence of PVP, it is believed that PVP selectively absorbs on the {010} facets and reduces the growth rate largely along the 〈010〉 direction and slightly along the 〈100〉 direction, leading to the formation of boardlike rectangle crystals40. In contrast, SC may preferentially bind to {011} and {110} facets more strongly than PVP, thus increasing the relative growth rates along both 〈100〉 and 〈001〉 directions. This phenomenon was evidenced by our observation that the introduction of SC led to an increase in both L and W of cuboids as well as the shape transformation of cuboids from rectangle to square boardlike. We, therefore, conclude that Fe3+, PVP, and SC serve as directing agents in the formation of shaped crystals and hence cuboids with tunable geometries.Three key factors including Fe−, and finally to Col phases via sedimentation of colloids from their ethanol suspensions. In particular, when dimethyl sulfoxide (DMSO), which has a density closer to silica than ethanol, was used, the relatively slow sedimentation of particles led to the formation of highly ordered crystal B phase, thanks to the extremely high uniformity of colloidal cuboids.Remarkably, monodisperse colloidal cuboids with various shapes resemble boardlike mesogens to assemble into various mesoscopic LC phases via slow sedimentation in solvent Fig. . It shouL-axis of cuboids is perpendicular to the smectic layer and their W- and T-axes randomly orient within the layers  characterization Fig. . In the ers Fig. . The forers Fig. . In compers Fig. . Cuboidsmal Fig. . This phmal Fig. . Typicalmal Fig. . Interesmal Fig. . In thismal Fig. . The resmal Fig. . The cryase Fig. . In thisase Fig. . POM imaase Fig. . For squase Fig. . The verase Fig. .θ which can be calculated as33:κ1 = L/T and κ2 = W/T, respectively. θ varies from −1 , via 0 , to 1 34. Specifically, rodlike colloids with less noticeable biaxial shape (θ > 0.05) were stabilized in SmAu phase, as they can rotate freely along the L-axis because of the relatively small energy barriers for their rotation within layers. Biaxial boardlike cuboids with θ around 0 preferred to form SmAb phase in ethanol and crystal B phase in DMSO. The formation of crystal B phase is ascribed to the high uniformity of colloidal particles, comparable to small molecular mesogens. As θ decreased to the range of −0.65 < θ ≤ −0.25, the biaxial shape was not sufficient to drive the formation of the SmAb phase. Instead, the cuboidal colloids were accommodated in N− phase. At θ ≤ −0.65, the particles turned into square platelike, resulting in the phase transition to Col phase. Overall, these results manifest that the rich mesophase polymorphism and the phase sequence of cuboidal colloids are strongly dependent on their shape biaxiality and sedimentation rate of colloids during assembly.We systematically investigated the effect of colloidal geometry on the LC behaviors of cuboidal colloids and mapped the five mesophases in a phase diagram Fig. . Moreove−, and Col phases. This work offers insights into the phase behavior of boardlike molecular or colloidal mesogens. Moreover, it provides a library of colloidal building blocks for the fabrication of functional materials with broad applications in such as photonic crystals  was prepared in a 10 ml glass vial. A 140 μl aqueous solution of 40 mg ml−1 Fe2(SO4)3 (Sigma-Aldrich) and a 500 μl of anhydrous ethanol (Pharmco-Aaper) were added into the glass vial and mixed thoroughly under vortex stirrer. The mixed solution was kept standing for 1 min to release gas bubbles. Then, a 60 μl aqueous solution of NH3  and a 50 μl of TEOS  were added into the solution in sequence. After each addition, the mixture was gently shaken for 30 s. The mixture was incubated at 25 °C for 4 h to allow the reaction complete.Typically, a solution containing 500 mg of PVP  and 5.0 ml of After the completion of the reaction, the solution containing synthesized colloidal cuboids was then centrifuged at 6000 rpm for 10 min. The supernatant was removed and the particles were washed three times with ethanol at 3000 rpm. Finally, the particles were centrifuged at 1000 rpm for 10 min to remove lightweight iron hydroxide small particle impurities.3 aqueous solution  were added and mixed. Then TEOS (50 μl) was added dropwise under stir.Typically, cuboidal colloids (10 mg) were dispersed in ethanol (5 ml) in a 10 ml glass vial. Deionized water  and NHPurified cuboids were dispersed in water upon vortex. The solution was then centrifuged at 4000 rpm for 10 min, and the cores are fully removed after three times wash with water.A dispersion of colloidal cuboids in ethanol or DMSO  was filled into a one-end-sealed glass tube with an outer diameter of 1.9 mm, inner diameter of 1.3 mm, and length of 100 mm (Stuart) and a rectangular capillary tube with a dimension of 0.2 × 2 × 50 mm (VitroCom). The other end of the tube was then heated and sealed by melting the glass. The tube was placed vertically to allow the slow sedimentation of colloidal particles for 1 month. Afterwards, the sediment of assembled colloids in the capillary tube was directly visualized and imaged under POM. For SEM characterization, the glass tube was broken from one end and the sediment was dried in air at room temperature.−1. POM images were obtained by using a Nikon Eclipse Ti-S microscope with polarizer.Colloidal cuboids and their assemblies were imaged by using a Hitachi SU-70 Schottky field emission gun SEM at an operation voltage of 10 kV. TEM images and EDS maps were taken by using a JEOL 2100 FEG TEM at an operation voltage of 200 kV. XRD patterns were recorded with a Bruker Smart1000  using Cu Kα radiation. FT-IR spectral measurement was performed on a Thermo Nicolet NEXUS 670 FTIR (32 scans) with a resolution of 4 cmSupplementary Information


Twinning is a fundamental mechanism behind the simultaneous increase of strength and ductility in medium- and high-entropy alloys, but its operation is not yet well understood, which limits their exploitation. Since many high-entropy alloys showing outstanding mechanical properties are actually thermodynamically unstable at ambient and cryogenic conditions, the observed twinning challenges the existing phenomenological and theoretical plasticity models. Here, we adopt a transparent approach based on effective energy barriers in combination with first-principle calculations to shed light on the origin of twinning in high-entropy alloys. We demonstrate that twinning can be the primary deformation mode in metastable face-centered cubic alloys with a fraction that surpasses the previously established upper limit. The present advance in plasticity of metals opens opportunities for tailoring the mechanical response in engineering materials by optimizing metastable twinning in high-entropy alloys. Twinning has been experimentally seen in high-entropy alloys, but understanding how it operates remains a challenge. Here, the authors show that twinning can be a primary deformation mechanism in three well-known medium- and high-entropy alloys that have unstable face-centered cubic lattices. The discovery of the advanced high-strength steels near the turn of the century was a pioneering step towards reaching such goals2. Recently, the strength–ductility tradeoff was also overcome by designing dual-phase high-entropy alloys (HEAs)3, which turned attention towards non-conventional alloys containing multi-principal elements in equal or nearly equal concentrations5. Successful single-phase solutions also exist such as the fracture-resistant HEAs6 and the exceptional damage-tolerance medium-entropy alloys (MEAs)7. Further, HEAs with surprising and unanticipated properties are being continuously put forward18. Parallel with the experimental efforts, computational modeling was also advanced to optimize compositions and reveal the atomic-level mechanisms19 behind the strength and ductility improvements.Designing strong, and at the same time, ductile metals has been among the most ambitious goals for metallurgists. The apparently conflicting mechanisms often resulted in grades compromising between strength and ductility. In spite of the early ideas revolving around Fe–Mn alloys21. There are several phenomenological plasticity models23 developed to understand the activation of twinning deformation in fcc metals. These models are based on the stacking fault energy (SFE) defined as the excess energy due to the stacking fault connecting the Shockley partial dislocations. For instance, austenitic steels with SFE higher than 10–16 mJ m−2 are predicted to show twinning26, whereas alloys with SFE below this critical value should exhibit deformation-induced phase transformation. Theoretical works also addressed the question of twinnability29, and they indicate twinning for fcc stable metals with twinning fraction below 50%27.The common denominator of the most promising routes over the strength–ductility dilemma is the tailored plastic deformation mechanism. Face-centered cubic (fcc) metals normally deform by dislocation glide resulting in good ductility and reduced strength. But in the aforementioned special alloys, the leading deformation modes are twinning and transformation induced plasticity. In particular, twinning was found to play an important role for the enhanced ductility and high strain hardening rate30. Thus, the sizable positive SFE required for twinning by the phenomenological models23 implies a thermodynamically stable fcc phase. Traditional alloys like austenitic steels31 belong to this group. Since the discovery of the first fcc HEAs based on 3d transition metals5, it was expected that these multi-component systems also follow a similar plastic deformation scenario. However, recent experimental works reported a pressure-induced fcc–hcp transition in CrMnFeCoNi, and the hcp phase was retained after decompression to ambient pressure33. The observed equilibrium phase boundary between the fcc and hcp phases was placed near 600 K33. For the multi-component alloys in question37, including CrMnFeCoNi, ab initio theory fully confirms the experimental findings by predicting a thermodynamically unstable fcc lattice at low temperature. Hence, both experiment and theory suggest that the metastable fcc CrMnFeCoNi alloy should possess very small positive or negative SFEs at ambient and cryogenic conditions. But such SFE values in connection with the phenomenological models23 clearly fail to explain the observed twinning6. Thus, the available experimental and theoretical evidences severely challenge the validity of the widely-used SFE-based plasticity models in the case of metastable HEAs27. Although this fundamental problem was recognized by several groups38, so far no unified solution has been put forward.Within the simplest model, the SFE corresponds to the energy difference between the hexagonal close-packed (hcp) and fcc structuresx80−MnxCo10Cr10 (30 ≤ x ≤ 45) and CrMnFeCoNi HEAs6, and CrCoNi MEA7, for which reliable experimental data for the deformation mechanisms exist. Based on the intrinsic energy barriers, we show that it is the particular chemistry that is primarily responsible for the emergence of twinning in metastable alloys, and decreasing temperature efficiently drives the system across various deformation modes. The inherent affine shear strain prior to slip, which is associated to the relatively large critical twinning stress characteristic to the present systems, further promotes twinning. Our results are expected to help optimizing the properties of engineering materials involving HEAs towards excellent strength–ductility combinations.Here, we employ first-principle quantum mechanical tools to resolve the twinning mechanism in metastable multi-component alloys. We select the representative Fe27. In these expressions, θ varies between 0° and 60° and reflects the effect of grain orientation relative to the resolved shear direction, γ-surface)39 and are determined from a series of ab initio calculations. According to the above EEBs, twinning is possible for θ (they possess similar Schmid factors). We mention that SF and TW can coexist in special alloys  due to the broadening of their boundary. For the sake of simplicity, in the following we do not always mention SL when discussing the SF and TW mechanisms.The leading deformation modes in fcc metals are stacking fault (SF), twinning (TW), and full-slip (SL). The active mode is decided by the competition between the corresponding effective energy barriers (EEBs): x80−MnxCo10Cr10 alloys as a function of temperature and composition. These alloys are reported to exhibit twinning or martensitic transformation depending on Mn content3. The room-temperature x ≥ 33, TW is favored against SF, whereas for x < 33 SF is activated. In addition, SL is present in both regimes with θ increasing towards 60° (not shown). These theoretical predictions are in perfect agreement with observations3 and confirm that the present theory correctly captures the observed trends of the deformation modes as a function of composition.Using the calculated EEBs, first we explore the deformation modes of the Fex80−MnxCo10Cr10 by varying x. Negative ΔF indicates a thermodynamically unstable fcc lattice, which turns out to be the case for all compositions considered here. By combining the behavior of ΔF with that of x80−MnxCo10Cr10 HEAs, the room-temperature twinning is associated with a metastable fcc phase for 33 ≤ x ≤ 50 (upper limit is estimated by extrapolating the slope of ΔF towards high Mn content). We emphasize that since these alloys possess negative or very small positive 23 would dictate SF rather than TW mechanism, opposed to the present theoretical and former experimental findings3. Actually, in these special alloys, F. Below, we refer to this phenomenon as metastable twinning (MTW).Figure 7. Figure F as a function of temperature for the Fe40Mn40Co10Cr10 HEA. Alloys with different Mn-levels follow qualitatively the same behavior and thus they are not included in the figure. At high temperature, the system is in the positive ΔF and TW + SL regime. Similar normal behavior is observed at very low temperature, where negative ΔF is associated with SF + SL. However, there exists a temperature interval where the Fex80−MnxCo10Cr10 HEAs behave unexpectedly: TW is activated in spite of the negative ΔF. For x = 35, 40, and 45, the abnormal interval includes room-temperature, whereas for x = 30 it is shifted above room-temperature. Similar calculated F versus temperature data for CrMnFeCoNi and CrCoNi alloys are shown in Fig. 41 , for the affine shear strain we obtain ~0.8% as the characteristic value45. The Fe40Mn40Co10Cr10 and CrCoNi alloys are expected to possess slightly smaller and larger strains, respectively, as suggested by their EEBs , MTW occurs for all temperatures below ~460 K, which explains the reported stress–strain behavior at cryogenic condition6. On the other hand, for CrMnFeCoNi, the calculation without shear strain predicts a martensitic transformation below ~300 K, which is consistent with the pressure-induced fcc–hcp phase transition33.For CrMnFeCoNi alloy, the measured critical twinning stress is around 235 ± 10 MPaEBs Fig. . The impF ≤ 0. Below, we reformulate the MTW condition using the SFE and related IMPs. An intrinsic stacking fault in an fcc lattice can be treated as a two-layer embryo with the hcp structure embedded in the fcc matrix. Accordingly, the SFE is often expressed as46A is the area of the stacking fault and σ* is the pseudo-interfacial energy between the hcp embryo and the fcc matrix. Hence, F values, for the present alloys we obtain σ* ≈ 4–9 mJ m−2, which compares well with 8–9 mJ m−2 reported for Fe–Cr–Ni alloys encompassing about 14–20% Cr and 16–20% Ni47. In applications, σ* is often considered to be independent of temperature48.So far we defined MTW as twinning deformation when Δ49 have 50, for which however the large negative 22. However, twinning can take place also when 51. Theoretical calculations show that for fcc metals and alloys, δ can be both positive and negative42, and its absolute value is usually around a few mJ m−2. Combining this constraint for twinning with the one for the lattice instability, for the condition of MTW, we obtain δ is below the pseudo-interfacial energy.In terms of IMPs, twinning can occur if 38, as well as for the paramagnetic Fe52 or the low-Ni austenitic stainless steels31, δ and σ* when changing the temperature, and thus the MTW mechanism might become effective within a specific temperature range. The sign of σ* is usually small , mostly alloys with very small or negative δ are expected to enter the MTW regime upon changing the temperature. Alloys with positive δ (but still below σ*), such as Fe–Cr–Ni31, are predicted to have a very narrow temperature interval for MTW. Finally, alloys with The above condition excludes from the MTW family, all metals and alloys, which possess 40Mn40Co10Cr10, CrMnFeCoNi, and CrCoNi as a function of affine shear strain and temperature. It is remarkable that all three alloys satisfy the 40Mn40Co10Cr10, 460 K for CrMnFeCoNi, and at all temperatures considered here in the case of CrCoNi. Hence, MTW should appear within certain temperature and strain intervals in all three multi-component alloys. These findings are in perfect line with the present theoretical predictions in Fig. 20.The two critical parameters for MTW, entclass1pt{minima40Mn40Co10Cr10 for temperatures between 100 and 500 K and three affine strain levels. In the stress-free case, MTW is present for −2, i.e., both negative and small positive SFE values are associated with TW. Increasing the affine shear strain, the maximum allowed σ*. Furthermore, the lowest SFE for which twinning is observed decreases strongly with strain and thus broadening the range for MTW. For 0.8% strain, which is close to the estimated value for CrMnFeCoNi, the lowest SFE where TW is the active deformation mode (in addition to SL) is −16.5 mJ m−2. The room-temperature SFE values for strains up to 0.8% vary between −1.3 and 2.3 mJ m−2, and lie well inside the MTW region. These SFEs are far below the empirical limit for twinning (10–16 mJ m−2) and confirm that classical SFE-based deformation models23 fail to account the observed twinning in this HEA3. In Fig. 40Mn40Co10Cr10. We mention that the deformation maps for CrMnFeCoNi and CrCoNi (not shown) are qualitatively the same as that in Fig. 22, in Fig. −2 (depending on the affine shear level), which agree well with 6–12 mJ m−2 obtained by correcting the empirical critical values of twinning26 by the estimated strain contribution near the partial dislocations53. However, in the deformation mode map for MEAs and HEAs, there is a broad MTW region, which broadens towards both positive and negative SFE values with increasing affine strain. In contrast, in the case of traditional alloys like austenitic stainless steels, the MTW region is very narrow or completely missing.In order to express the deformation modes in terms of the experimentally accessible SFE, in Fig. δ = 0), we obtain −2, meanwhile in metals with δ > 0, the twinning fraction turns below 50%. For instance, for Fe68Cr20Ni12 alloy31, δ = 7 mJ m−2 and −2 give 40Mn40Co10Cr10 yield δ = −4 mJ m−2 (neglecting the affine strain) and thus The present theoretical exploration opens opportunities to fine tune the plastic flow mechanisms in HEAs. One important example is the enhanced twinning probability (x80−MnxCo10Cr10, CrMnFeCoNi, and CrCoNi, in which twinning was reported to be a fundamental deformation mechanism. Both theoretical and experimental results suggest that the studied alloys possess a thermodynamically unstable fcc lattice at ambient and cryogenic conditions, which questions the phenomenological plasticity models. In contrast to those models, the present theory demonstrates that twinning can be the primary deformation mode for metastable fcc alloys with a twinning fraction that surpasses the previously established upper limit. It is found that small affine shear strain can further amplify the MTW phenomenon. The disclosed behavior of the present MEAs and HEAs calls for a critical revision of the previously adopted design criteria regarding twinning-induced plasticity. Our quantum theory aided understanding of the plasticity shows that a precise control of the deformation mechanisms is feasible in these new families of multi-component alloys, which can ultimately facilitate the optimal harvesting of their properties.We have investigated the plastic deformation in multi-component alloys and presented a transparent atomic-level theory for the MTW mechanism. The theory is based on the EEBs derived from first-principle calculations. We focus on the high strength and high ductility alloys, Fe54 and the exact muffin–tin orbitals formalism55. The Kohn–Sham equations were solved within the scalar-relativistic and soft-core schemes. Since the Curie temperatures estimated via the mean field approximation56 are below 77 K, all alloys were described in the paramagnetic state modeled by the disordered local magnetic moment approximation57. The chemical and magnetic disorders were taken into account using the coherent potential approximation55. The free energies included the lattice expansion and magnetic entropy terms. For a randomly oriented polycrystalline alloy, the fraction of grains showing TW was estimated from the IMPs via27The present calculations were based on density-functional theory formulated within the generalized gradient approximationThe data that support the findings of this study are available from the corresponding authors upon reasonable request.Supplementary Information


Fin whale δ15N values were higher than those recorded in blue and sei whales, reflecting feeding at higher trophic levels. Whales' skin δ15N and δ13C values did not reflect prey from high-latitude summer foraging grounds; instead mixing models identified tropical or subtropical regions as the most likely feeding areas for all species during winter and spring. Yet, differences in δ13C values among whale species suggest use of different regions within this range. Blue and sei whales primarily used resources from the Northwest African upwelling and pelagic tropical/subtropical regions, while fin whales fed off Iberia. However, determining feeding habitats from stable isotope values remains difficult. In conclusion, winter feeding appears common among North Atlantic blue, fin and sei whales, and may play a crucial role in determining their winter distribution. A better understanding of winter feeding behaviour is therefore fundamental for the effective conservation of these species.Knowing the migratory movements and behaviour of baleen whales is fundamental to understanding their ecology. We compared δ It is a common population-level strategy across many taxa that emerges from individuals' movements that are probably aimed at maximizing fitness . The indMegaptera novaeangliae), fin  and blue  whales, some individuals remain at high to mid-latitudes during the breeding season, or in tropical waters during the feeding period [The classical model of baleen whale migration assumes that most individuals migrate between high-latitude productive summer feeding areas and subtropical to tropical breeding winter areas, where feeding is believed to be absent or severely reduced –9. Howevg period –14. Otheg period –18. Simig period –21. HoweBalaenoptera borealis) whales encountered off the Azores. Blue and fin whales suspended their northward migration and remained at temperate latitudes for periods of up to a few months. While at mid-latitudes, individuals spent a substantial proportion of their time engaged in behaviours inferred as foraging [Earlier studies ,22 emploforaging . In contIndeed, despite substantial progress in tracking technology, deploying satellite tags in baleen whales that remain on the animal for the entire annual migratory cycle remains challenging ,24.Stable isotope analysis (SIA) can partially circumvent these challenges and yield insights into the migration and trophic ecology of whales. Such an approach is based upon two premises: the stable isotope composition of an organism reflects that of its food , and the15N/14N, δ15N) usually increase by 2–4‰ per trophic level [15N value is commonly used to infer the trophic positions of consumers [13C/12C, δ13C) change little (approx. 1‰) with trophic level but can be a useful indicator of the spatial origin of carbon assimilated by consumers [The ratios of heavy to light isotopes of elements in the tissues of consumers reflect those of the food assimilated over a period of time , with a slight difference . This diic level ,31; henconsumers . In contonsumers .13C values of primary producers are strongly influenced by the δ13C value of dissolved inorganic carbon, temperature, CO2 drawdown, as well as the size, composition and growth rates of the phytoplankton community [13C values vary substantially with latitude, and among primary production pathways, such as phytoplankton (−24‰ to −18‰), macrophytes (−27‰ to −8‰) and seagrasses (−15‰ to −3‰), and productivity regimes  [13C values tend to decline from coastal and benthic sources to offshore and pelagic sources, and from northern to southern latitudes within each hemisphere [15N also varies geographically, depending on the sources of nitrogen used by primary producers, nitrogen cycling and assimilation processes [15N values of zooplankton in the upper 200 m decrease with latitude and reach the lowest values in oligotrophic subtropical and tropical waters [δommunity ,34. Consophytes −‰ to −8‰ to −18‰) ,35,36. Iophytes −‰ to −8‰ rocesses –39. In tophytes −‰ to −8‰ 15N and δ13C values in biopsy-derived skin samples and faecal material have been employed to infer diet and trophic level, niche partitioning within and among species, as well as population structure and migratory movements . –45. 15N ts  was estimated at approximately 24 days and approximately 48 days for δ13C and δ15N values, respectively, and complete turnover in approximately 100 days for δ13C and approximately 200 days for δ15N values [15N values was estimated at 5.4 months [13C turnover could not be assessed, by similarity to dolphins it is reasonable to assume that they may be in the range of approximately three months. Therefore, we assumed that skin samples collected mostly between March and June reflected isotopic ratios acquired during early winter to early spring. In addition to the skin samples, we also analysed the δ15N and δ13C values in a small number of faecal samples collected opportunistically. Stable isotopes in whale faeces represent a record of recently (from hours to 1–2 days) ingested prey [In this study, SIA and Bayesian mixing models were used to assess (i) if blue, fin and sei whales feed at lower latitudes and (ii) their habitat use during winter and spring. For that, we estimated the δlacement . Half-liN values ,49. In f4 months  and, altted prey  and, in 2.2.1.Skin and faecal samples from blue, fin and sei whales were collected between 2002 and 2014 in the Archipelago of the Azores, Portugal . Skin sa2.2.13C and typically have δ13C values that are more negative than those of proteins and carbohydrates within an organism [13C values is to remove lipids from tissues by chemical lipid extraction [15N values of tissues, although the magnitude and direction of change is species and tissue dependent. In the case of baleen whale skin, some studies reported a significant decline in δ15N values after lipid extraction [15N values for the same whale species [15N in baleen whale skin due to lipid extraction were generally small and within the analytical precision typical of δ15N measurements [15N is to measure δ13C in lipid-extracted tissue and δ15N in non-extracted tissue [Lipids are depleted in organism , which ctraction . Howevertraction , while o species . Similar species , changesurements ,54. None15N and δ13C were analysed in lipid-extracted skin. Lipid-extracted samples were freeze dried and ground with a mortar and pestle into a homogeneous fine powder. Approximately 1 mg of powder sample was used for δ13C and δ15N measurements. Analyses were carried out using isotope ratio mass spectrometers (Thermo Finnigan Delta Plus XP IRMS and Finnigan MAT Delta C) coupled to a Costech 4010 or a Flash EA-1112 elemental analyser. Stable isotope ratios were expressed conventionally as δ (‰) relative to the Pee Dee Belemnite (PDB) standard for the δ13C value and atmospheric nitrogen (AIR) for the δ15N value.Hence, lipids were extracted from skin samples using repeated rinses with 2 : 1 chloroform : methanol solution  and δ15N2.3.−1. The sex of each sampled whale was determined by the amplification of the ZFY and ZFX specific sequences [Genomic DNA was extracted using QIAGEN DNEasy™ extraction columns for animal tissue, following the manufacturer's instructions (QIAGEN Inc.). The quality of the DNA was checked visually by electrophoresis through a 0.7% agarose gel and quantified with a Qubit™ following the manufacturer's instructions (Thermo Fisher Scientific Inc.). DNA extractions were normalized to a concentration of 10 ng DNA µlequences .2.4.15N and δ13C values. The PERMANOVA is a non-parametric method that analyses the variance of multivariate data based on pairwise measures of dissimilarity or distance [15N and δ13C values, with three fixed factors  and employing type III sum of squares. Only years with sample sizes greater than or equal to 2 were included in the analysis. Subsequently, univariate ANOVAs, followed by Tukey's post hoc tests, were conducted to further assess the effects of significant variables identified in the PERMANOVA analysis on δ15N and δ13C values separately. Prior to analyses, the assumptions of data normality and homogeneity of variances were checked using Kolmogorov–Smirnov and Levene's tests, respectively.Only isotopic ratios measured from whale skin were used in the following analyses. For each whale species, a permutational multivariate analysis of variance (PERMANOVA)  was useddistance . The PER15N and δ13C values between whale species were assessed using a one-way PERMANOVA with type III sum of squares using the normalized Euclidean distances. Standard ellipse areas corrected for small sample size (SEAC) were estimated to determine the isotopic niche space of each whale species and assess overlap in niche space among them. The SEAC represents 40% of the total area encompassing all samples in the δ13C–δ15N biplot space and is less sensitive to outliers and unbiased with respect to sample sizes [C was estimated for each species pair serving as a proxy for trophic and prey overlap.Differences in δle sizes . The perPERMANOVA analyses were performed using PERMANOVA+, the add-on for PRIMER-E v6 , and sta2.5.Bayesian isotope mixing models were employed to identify the most probable feeding areas of blue, fin and sei whales during winter and early spring. The potential range of these whales was divided into general regions based on knowledge of their distribution in the North Atlantic and of the movements of whales migrating through the Azores . These regions were: Northwest Atlantic (NWA), including the Gulf of St. Lawrence, Newfoundland, western and southwestern Greenland; Northeast Atlantic (NEA), extending from southeastern Greenland to the Barents Sea; Iberia (IB); Bay of Biscay (BB); western Mediterranean (MED); Azores (AZ); pelagic waters off Northwest Africa (NAF), from Cape Verde to Liberia and offshore to the Guinea Dome; and the upwelling region along the Northwest Africa coast (NAF-UPW), from Morocco to Senegal .15N and δ13C values can also show significant seasonal variations at regional scales [We identified stable isotope ratios of potential prey taxa within these regions from the literature and from data repositories (PANGEA) , and that included both δ15N and δ13C values, measures of isotope variability and sample sizes used. For each region, prey group and season, we estimated the mean  prey isotopic value by summing the means and variances of all the available data, weighted by sample size. These values were used as input parameters in mixing models.We only included prey isotopic data from studies that accounted for the effect of lipids on 15N and δ13C values of all whales (raw data) and of prey sources (entered as mean ± s.d.), and a diet–skin discrimination factor estimated for fin whales (2.82 ± 0.30‰ for δ15N and 1.28 ± 0.38‰ for δ13C) [13C values observed in individuals sampled during spring and summer (see results below). Fin and blue whales were each treated as a single group. Fin whale models were performed excluding whales sampled during autumn.Stable isotope mixing models were run in R using the SIMMR v. 0.3 package . Parametor δ13C) . We perf3.n = 17), fin (n = 42) and sei (n = 36) whales (n = 72). The remaining samples were collected in summer , autumn  and winter . No deviations from parity in sex ratios were observed in any whale species . Additionally, 13 faecal samples were collected from blue (n = 1), fin (n = 10) and sei (n = 2) whales  whales .Table 13.1.13C values and from 8.0‰ to 11.0‰ for δ15N values. Fin whale skin ranged from –20.4‰ to –17.0‰ in δ13C values and from 8.6‰ to 11.9‰ in δ15N values, while sei whale skin ranged from –19.3‰ to –15.9‰ in δ13C values and from 7.3‰ to 10.3‰ in δ15N values. All whale species displayed slightly higher variability in δ13C values than in δ15N values  relative to fin whales sampled during the spring and summer .No significant effect of season on fin whale isotope ratios was detected . Howeverd summer , univarier years . Whales rom 2014 . While t15N values were not statistically significant but δ15N values differed significantly among years . In 2014, sei whales were more depleted in 15N than whales sampled in 2005, 2008 and 2009 . The highest δ13C values were measured in sei whales sampled in summer and the lowest values in whales sampled in autumn, whereas animals from spring displayed intermediate values. Moreover, δ13C values increased with sampling day for sei whales sampled during spring and summer , such that whales sampled in mid-summer had δ13C values 2.6‰ higher than those sampled in mid-spring   .Figure 3.2.13C values. Only fin and sei whales showed significant differences in δ15N values, with fin whales presenting higher values (9.5 ± 0.73‰) than sei whales (9.01 ± 0.62‰).Blue, fin and sei whale skin showed distinct isotopic ratios . Univariate tests followed by pairwise comparisons indicated that sei whales (–17.5 ± 1.01‰), blue whales (–18.7 ± 1.02‰) and fin whales (–19.4 ± 0.76‰) were all significantly different from each other in δC and fin whales the smallest SEAC . Species differences in isotopic space were mostly in δ13C values although δ15N values also contributed to the separation of SEAC of blue and fin whales.Blue whales had the largest estimated SEAest SEAC . There w15N values (6.2 ± 0.78‰) than those of sei whales (4.8 ± 1.33‰) but overlapped those of blue whales (5.3‰). In contrast, faecal δ13C values were similar in all three species . Isotopic ratios of prey from the remaining regions also formed relatively distinct clusters, but there was considerable within-region variability in the δ15N values of prey sampled during the spring and autumn . Overall, the highest δ13C and the lowest δ15N values of copepods and euphausiids were observed in the NAF-UPW and NAF regions, although there was some overlap with other regions. Isotopic ratios of prey from the AZ, IB, BB and MED were intermediate between those of NWA/NEA and NAF-UPW/NAF. In the NAF-UPW and NAF, spring copepods and euphausiids had significantly higher δ15N values than those from autumn, while the opposite pattern was detected in euphausiids from IB.Copepods and euphausiids sampled during summer in the NWA and NEA were highly 3.4.15N and δ13C values of blue, fin and sei whales were outside the range of isotopic values of prey (adjusted to account for trophic discrimination) from NWA and NEA . However, the model was poorly resolved as the 95% credibility intervals overlapped substantially across nearly all sources. Fin whale skin primarily reflected euphausiids from the IB during spring , with the remaining sources contributing negligible amounts (3–5%) to fin whale diet . Sei whale mainly used copepods from NAF-UPW, with a lower contribution from NAF and AZ . The relative contribution of these regions varied slightly between sei whales sampled in spring and summer. Sei whales sampled in spring exploited proportionally more the NAF-UPW region during autumn, while those sampled in summer showed increased use of NAF-UPW during spring, as well as of NAF and AZ.Mixing model results indicate that blue whales mainly used food resources available in NAF and NAF-UPW during autumn, and in the AZ during spring  among blue whales sampled during the same year and month that were not matched by differences in δ15N values. Likewise, the temporal variation in skin isotopic ratios observed in fin and sei whales was higher for δ13C than for δ15N values, and changes in δ13C values were decoupled from variations in δ15N values. These findings indicate that differences in diet may not be the main explanation for the isotopic variation in whale species, and instead suggest that whales foraged in spatially and/or temporally distinct food webs.There was no evidence of annual or seasonal differences in isotope ratio for blue whales sampled in the Azores, probably because of the small number of samples analysed. However, we observed large differences in skin δ13C values among blue whales sampled just a few days apart suggests that individuals within the population foraged across a strong gradient in baseline δ13C values. Such gradients are often associated with sharp transitions in biogeochemistry, oceanography and productivity across major biogeographic domains [The high variability observed in δ domains ,65,66.13C and δ15N values could not be related to those of potential prey. Nevertheless, several different hypotheses could explain the decrease in fin whale δ13C values over the years. Long-term changes in sea-surface temperature, upwelling intensity and zooplankton composition documented off the Iberian coast [13C values and ultimately those of fin whales. However, these oceanographic and biological changes were evident at decadal scales. Fluctuations between successive years were small and unlikely to cause significant trends in baseline δ13C values. Similarly, the estimated rate of change in δ13C of dissolved inorganic carbon (DIC) in the North Atlantic attributable to the Suess effect  ranges between −0.007‰ per year in polar regions and −0.025‰ per year in subtropical regions [13C values. The lack of seasonal differences in fin whale isotopic ratios also indicates that time of sampling was unlikely to be the cause of the observed inter-annual variation. Hence, a change in foraging location, towards more 13C-depleted regions, appears to be the most parsimonious explanation for the observed decline in δ13C values in fin whales.Although statistically significant, inter-annual differences in fin whale isotope ratios should be treated with caution due to small sample sizes in most years. Additionally, owing to the lack of prey samples from all fin whale sampling years, annual variations in skin δan coast ,68  concentration  during the development of upwelling-induced and spring blooms [13C and δ15N values than individuals sampled during summer. There may be several explanations for the lack of differences in δ15N values: foraging occurred before the exhaustion of the 14N pool and the subsequent increase in δ15N values in phytoplankton; changes in δ15N values at the food-web base were not immediately reflected in copepods owing to their longer turnover times [15N than δ13C values in whale skin tissue [Sei whale isotopic ratios varied little between years and mostly with season. The most evident pattern was a progressive increase in δg blooms ,72,73. Aer times ; and lonn tissue , becausen tissue . Seasona13C values recorded for the species. At the time of sampling, these two whales seemed to be migrating to the wintering habitats, as indicated by the satellite tracking data from one of these whales that was heading towards the Canary islands when the tag stopped transmitting [The two sei whales sampled in late September had the lowest δsmitting . Hence, We did not find differences in stable isotope values between male and female blue, fin and sei whales. Differences in trophic niche between male and female baleen whales observed outside the breeding season have been attributed to spatial segregation and dietary differences between the sexes, possibly reflecting intra-specific competition and individual or social preferences . Male an4.2.15N values than that of sei (0.53‰) and blue whales (0.44‰), although the differences from blue whales were not statistically significant. δ15N values mainly reflect the trophic position of consumers [Meganyctiphanes norvegica and Thysanoessa raschii) but they also consume a variety of small epipelagic and mesopelagic schooling fish [M. norvegica, T. raschii and Thysanoessa inermis), whereas sei whales feed primarily on calanoid copepods and to a lesser extent on euphausiids [15N values of copepods were consistently lower (by 0.2–1.42‰) than those of euphausiids sampled in the same region and season. Thus, the higher reliance of sei whales on copepods would explain their lower δ15N values relative to fin and blue whales.Despite the intra-specific variability in isotopic ratios observed in blue, fin and sei whales, the three species occupied distinct isotopic spaces and showed different mean isotopic ratios. Fin whale skin had higher δonsumers . Hence, ing fish ,62. Bluehausiids ,62. A co15N values. This was in clear contrast with studies from feeding grounds in the Gulf of St. Lawrence where δ15N values in fin whales were found to be 2‰ higher than those in blue whales [15N values observed among whale species in our study may partly be explained by exploitation of distinct isotope baselines, diluting variability in δ15N values from dietary differences.Despite these differences, there was a remarkable overlap in nitrogen isotope ratios in fin, blue and sei whales, where most individuals clustered within a narrow range (approx. 1.5‰) of δe whales . The sma13C values, on average approximately 2‰ and approximately 1‰ lower than values detected in sei and blue whales, respectively. The low δ13C values in fin whales indicate a majority of carbon derived from pelagic phytoplankton-based food webs or feeding at high-intermediate latitudes [13C values seen in sei whales suggest increased foraging in systems characterized by relatively high productivity. Blue whales showed the largest range in δ13C values and a mean δ13C that was intermediate between that of fin and sei whales. Taken together these results indicate that blue whales use a wider range of isotopically distinct habitats than the other two species.Fin whale skin had the lowest δatitudes ,36,72. C4.3.15N and δ13C values of copepods and euphausiids compiled from the literature agreed well with broad geographical patterns of both isotopes in the North Atlantic Ocean, and were consistent with latitudinal and longitudinal trends in oceanic productivity and nutrient sources [The regional δ sources ,72.15N and enriched in 13C compared with copepods from the NWA and NEA. Blue and fin whales foraged on euphausiids with lower δ15N and slightly higher δ13C values than those found during the summer in NWA and NEA. Such isotopic ratios are characteristic of low-latitude food webs. The outcome of the mixing model assessments suggests that whale isotope ratios primarily reflected prey from subtropical and tropical regions, and clearly demonstrates that blue, fin and sei whales fed during the winter migration.The skin isotopic composition in blue, fin and sei whales sampled in the Azores did not reflect that of prey consumed during summer in the NWA and NEA foraging grounds. This conclusion is supported by the results of the mixing models, which detected a minimal contribution of prey from these areas to the isotopic ratios of whales. Our results indicate that sei whales recently foraged in food webs that are considerably depleted in 15N values in fin whales sampled off the Azores were lower than values reported in fin whales sampled in the Gulf of St. Lawrence (–2.86‰) [13C relative to individuals from higher latitudes , whereas blue whales had values similar to those estimated in St. Lawrence blue whales. While this pattern contradicts the negative trend in baseline and prey δ13C values with latitude [13C-rich inshore systems [We could not find information on isotope ratios of North Atlantic sei whales to compare with our results but the isotopic composition of blue and fin whales sampled at higher latitude habitats supports these findings. The mean δ(–2.86‰)  and Celt(–2.86‰) . The sam(–2.86‰) , althouglatitude  it is co systems ,54.4.4.13C values were not well bounded by regional isotopic values. In the case of sei whales, this affected only the summer samples . This implies that either prey from important foraging regions was missing or prey from the regions included were not adequately represented. Although we cannot dismiss the first hypothesis, prey isotopic data from tropical and subtropical regions were limited to a few taxa and years, and are unlikely to represent the range of prey items consumed by whales and their annual isotopic variability. More importantly, we did not have access to prey isotopic data from the winter, which presumably is reflected in the isotopic composition of whales sampled during spring. Additionally, by combining prey taxa into groups and broad geographical regions, we increased the amount of isotopic variation of regional prey sources and the degree of overlap between them. In conclusion, the results obtained from the mixing model assessment might change with additional samples from these and possibly other regions. Nevertheless, these results help shed light on the large-scale winter habitat use of each whale species.The use of Bayesian mixing models enabled an assessment of the relative contribution of different regional sources to the diet of baleen whales. Yet, the results and discriminatory power of mixing models strongly depend on the number of prey sources and the isotopic separation among them, and can be biased by missing prey sources . In blue13C value consistent with foraging on open-ocean phytoplankton sources. The relatively high dietary contribution of the NAF-UPW to blue whales was explained by the two whales with a highly enriched 13C. Although these individuals were clear outliers in our sample, whaling records and historic and contemporary sightings support the occurrence of blue whales in the upwelling region off Mauritania in winter [13C observed from spring to summer samples and is probably an artefact from the lack of prey sources with δ13C values as high as those of sei whales. Use of the Northwest African upwelling  by sei whales is well documented in the literature [The model results indicate that blue and sei whales foraged primarily in food webs supported by the NAF-UPW system, as well as in less productive pelagic food webs within tropical and subtropical regions. The majority of blue whales showed a δn winter –80. Preyterature –80 and aterature . We suggterature .13C values, composed mainly of samples from 2014. As fin whale δ13C values varied significantly among years, this implies that model results only represent regional contributions to fin whale diet in 2014. Nevertheless, these findings suggest a previously undocumented migratory link between the Northeast Atlantic and the Central Atlantic. It remains unclear if the connectivity between the two regions detected only in 2014 represents a recent shift in fin whale movement patterns. Finally, model results corroborate our previous interpretation of isotopic ratios and clearly show that fin whales did not feed in the NAF-UPW, in agreement with the scarcity of sightings in the area [The results of the fin whale model indicate an overwhelming predominance of the Iberia region to fin whale diet, probably because of the influence of the dense cluster of low δthe area .4.5.15N  in plasma and newly formed tissues, as a consequence of protein synthesis using 15N-enriched amino acids derived from catabolism of endogenous protein [13C appears less sensitive to nutritional stress [13C values in hibernating bears, as a result of the mobilization of lipid stores during fasting [Physiological changes associated with periods of fasting or nutritional restriction can affect stable isotope dynamics, and hence the isotopic ratios in different animal tissues, complicating interpretation of isotopic analyses. The most commonly reported effect is an increase in δ protein . Althougl stress , some st fasting .15N values in the baleen plates of fin whales and both δ15N and δ13C values in baleen of bowhead whales  increased during the summer, a period of intensive feeding, and declined during the winter, when whales were assumed to feed less [15N increase was attributed to the whale's ability to maintain a positive nitrogen balance through limited winter feeding, reduced excretion rates and reliance on lipid catabolism to fulfil energetic and physiological needs, thereby avoiding protein mobilization [15N values would reflect variation in baselines between summer and winter grounds [Contrary to expectations, the δeed less ,84,85. T15N values and the high discrepancy to δ15N values of prey and conspecifics from higher latitudes suggest that nitrogen in whale skin was derived mostly from dietary rather than endogenous sources, even during periods of food restriction. On the other hand, non-essential amino acids that are major components of skin tissue can be synthesized from lipid-derived carbons [13C-depleted relative to proteins [13C values lower than those of prey from northern foraging grounds. Again, our results contradict these predictions and indicate that skin δ13C values reflected recently consumed prey. Overall, these findings show that δ15N and δ13C values in blue, fin and sei whale skin were not driven by physiological changes associated with periods of food restriction.Lipids and carbohydrates contain negligible amounts of nitrogen , and ski carbons  that cou carbons  and thatproteins , incorpo4.6.15N values in whale faeces were slightly lower (0.1–1.1‰) than those in euphausiids and copepods collected at the Azores, probably because of selective removal of lighter nitrogen during digestion and assimilation [15N value and those of sei whales the lowest, indicating that sei whales feed at a lower trophic level than fin and blue whales. Unsurprisingly, δ13C values were similar among all species and agreed well with prey values from the regional pelagic food webs.Analyses of faeces are indicative of recent diet  and we amilation . Similar5.Our work provides strong evidence that North Atlantic blue, fin and sei whales migrating through central Atlantic waters feed during the winter or early spring in tropical and subtropical waters, and that this strategy is more prevalent among migratory whales than is currently accepted. Although the significance of winter feeding to the overall energetic budget of the animals remains unclear, these results contribute to a growing body of evidence suggesting that baleen whale migration is much more complex than previously assumed and cannot be reduced to a simple ‘feast and famine’ paradigm.n = 7) instrumented with satellite tags in the Azores during spring and early summer (April–June) migrated to the Labrador Sea [13C increased value, similar to the majority of sei whales analysed. Together these results showed that part of the sei whale population feeding in the Labrador Sea in the summer most likely originate from the wintering grounds off the Northwest African coast, as proposed earlier [Our findings support a distributional range for sei whales spanning across the North Atlantic basin. All sei whales (ador Sea ,89. Isot earlier .Finally, given projected changes in the upwelling system off Northwest Africa under climate change , the posSIA is increasingly used to examine trophic ecology and movements of baleen whales. These studies are generally based on isotopic measurements along the length of baleen plates, a tissue that is metabolically inert after synthesis and therefore can retain long-term records of feeding patterns. Collection of baleen plates depends on the availability of stranded specimens, making it difficult to obtain sufficient numbers of samples to document present–day movement patterns at the population level. We believe that the analysis of skin from biopsy samples collected from multiple times of the year and locations across the populations' range offers an alternative to analysis of baleen plates. Still, employing stable isotopes to track the migration and diet of baleen whales will require a better understanding of the factors affecting isotopic incorporation and trophic discrimination between whale tissues and their diet, as well as more detailed marine isoscapes and prey isotope ratios.


Arising from Richard N. Holdaway et al. Nature Communications 10.1038/s41467-018-06357-0 (2018)1 deposit is an isochronous marker bed that spans much of New Zealand’s North Island and pre-dates human arrival2. Holdaway et al.3 (HDK18 hereafter) propose that the current Taupo eruption date is inaccurate, and that the eruption occurred decades to two centuries after the published wiggle-match estimate of 232 ± 10 CE (2 SD)4 derived from a tanekaha (Phyllocladus trichomanoides) tree at the Pureora buried forest site6. HDK18 propose that trees growing at Pureora (and other near-source areas) that were killed and buried by the climactic ignimbrite event were affected by 14C-depleted (magmatic) CO2. HDK18’s proposal utilises a wide range of published 14C data, but their work results in assertions that are implausible. Four parts to their hypothesis are considered here.The Taupo eruption14C-date compilation used by HDK18 to claim that the Pureora and other near-source dates are anomalously old is flawed. The dataset used to construct HDK18’s Fig. 7 on Taupo eruptives from various sites  were not included. Most of the dates used in the figure have large errors and calibrated mean values extend between 650 CE and −100 CE, making them statistically indistinguishable and undermining the significance of any purported best fit correlation. This wide range of ages was a principal reason why wiggle-match dating of the Pureora buried forest logs was undertaken4. Ages in HDK18  show that the Rafter and Waikato laboratory analyses, undertaken on wood derived from the same tanekaha tree-ring chronology6, have a systematic offset, with Rafter analyses, which dominate HDK18  to derive a date for the eruption of 232 ± 10 CE4.The 10 at Kaipo bog, far removed from any possible magmatic 14C contamination3, which incorporates the Taupo eruption deposits. Here, stratigraphically ordered, independent age points  were used10 to derive dates (not cited by HDK18) for the Taupo layer of 231 ± 12 CE  and 251 ± 51 CE with a weighted-mean date of 240 CE (Bacon-software-derived), statistically identical to the Pureora wiggle-match estimate4.Relationships between the dates in HDK18’s Supplementary Table S1 , Supplementary Fig. S2  and the Taupo eruption deposits are also unclear, with the stratigraphic context often lacking, impairing the value of the age estimates. An example of best practice is from a section14C-depleted magmatic CO2 on reservoir ages in Lake Taupo (and the Waikato River draining the lake) is documented11. HDK18 present 14C dates of organic materials from this area, i.e., within 60 km of the Taupo eruption source , and propose that these dates are biased towards older ages by CO2 degassed from groundwater. We discount this proposition at the Pureora forest site for several reasons. First, deep, 14C-depleted groundwater is most unlikely to have affected the Pureora site, as it lies at 550 m above sea level5, in a separate catchment from that of the Waikato River, and is ~300 m above and 20 km distant from the Waikato River at its nearest point. Second, the site is ~200 m above the level of Lake Taupo and lies west of the watershed between it and the Taupo basin. Groundwater at the site is sourced from local rainfall . Third, the Pureora area also shows no traces of young faulting12 that could have channelled putative magmatic CO2. Fourth, the mechanism of gaseous exchange to introduce 14C-depleted carbon into groundwater at the Pureora site is most unlikely. Groundwater flow at the site will be dominated by vertically downwards flow of rainfall recharge from the soil layers to deeper units and thus atmospheric CO2 must dominate carbon dioxide flux at the site. The notion that magmatic carbon could be introduced into groundwater of the Pureora site from magmatic sources beneath Taupo volcano , or somehow be introduced (against gravity) from the Waikato River water, is implausible.The potential impact of injected 14C levels plateaued or declined as the eruption approached , and that after ~125 years , linear relationships with the actual tree age broke down: the tree continued to grow but 14C ages of the newly accreted wood were static (p. 4). However, the fitting of straight-line functions to 14C concentrations is meaningless, as non-linearity in 14C levels is universally recognised and underpins international calibration curves  and wiggle matching for age correlations4. Here we re-plot the Pureora tanekaha 14C data against known calendar-age data from Northland (northernmost North Island) kauri14 and Tasmanian huon pine (Lagarostrobos franklinii)15  propose a trend of lowered 13C record is flawed for two reasons. First, the Pureora tanekaha did not have at least 50 inner rings sampled, hence the lack of the so-called juvenile effect  is also not correct. For example, the outermost Pureora tanekaha rings yield cellulose δ13C values ~2‰ lower than the outermost rings from a kauri tree (e.g., Supplementary Fig. 13C values are neither anomalously high nor do they reflect any magmatic carbon input.HDK18’s analysis of the Pureora tanekaha tree δ3 that the Taupo eruption is decades to centuries younger than 232 ± 10 CE is unsound. Although 14C-depleted materials are associated with magmatic degassing11, the context and consistency of any radiocarbon dates indicate whether a robust and accurate age estimate has been attained. The 250-year 14C wiggle-match against SHCal13 presented here reinforces the view that 232 ± 10 CE4 remains the most accurate and precise age estimate for the Taupo eruption, and we conclude there is no evidence for anomalously older ages near the Taupo volcano. We re-assert that radiocarbon wiggle matching to refine volcanic event chronologies, especially where sequential 14C dates and Bayesian modelling form the basis of the event timing, remains an accurate and invaluable dating tool.In conclusion, HDK18’s proposalSupplementary Information