BUSCADOR RECOLECTA

Repository containing derived data for the manuscript 'Global collision-risk hotspots of marine traffic and the world's largest fish, the whale shark'., Peer reviewed

Predicted genes corresponding to the four most common enzymes present in photosynthetic organisms: NADH:ubiquinone reductase (H+-translocating), N-acetyl-gamma-glutamyl-phosphate reductase, DNA-directed RNA polymerase and non-specific serine/threonine protein kinase of 174 metagenomes sequenced during the Malaspina 2010 global expedition., Peer reviewed

The dataset is comprised of: downwelling diffuse attenuation coefficients, Z10%, aCDOM and ap., Peer reviewed

The dataset is comprised of: downwelling diffuse attenuation coefficients, Z10%, aCDOM and ap, Peer reviewed

Contents of this file: Figures S1 to S8 and Table S1. -- Figure S1. CDOM absorption coefficients (aCDOM, in m-1) at UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina 2010 Expedition. Reported values are depth-weighted averages from surface waters (3 m depth) down to the 20% PAR depth. -- Figure S2. Results of the Dunn’s tests, that were performed after Kruskal-Wallis tests to identify if aCDOM (top row), ap (middle row) and ap as % of anw (bottom row) at 305 nm (left column), 313 nm (middle column) and 320 nm (right column) varied significantly (p<0.05) between Longhurst provinces during the Malaspina 2010 Expedition. For a description of the Longhurst province codes, see Fig. 1. -- Figure S3. Particulate absorption coefficients (ap, in m-1) at UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina Expedition. Reported values are depth-weighted averages from surface waters (3 m depth) down to the 20% PAR depth. -- Figure S4. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina 2010 Circumnavigation. -- Figure S5. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the UV-A wavelengths 340 nm (top panel), 380 nm (middle panel), and 395 nm (bottom panel) measured during the Malaspina 2010 Expedition. -- Figure S6. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the integrated PAR spectrum (400–700 nm) measured during the Malaspina 2010 Expedition. -- Figure S7. Results of the Dunn’s tests, that were performed after Kruskal-Wallis tests to identify if the downwelling diffuse attenuation coefficient (Kd) at 305, 313, 320, 340 nm varied significantly (p <0.05) between Longhurst provinces during the Malaspina 2010 Expedition. For a description of the Longhurst provinces code, see Fig. 1. -- Figure S8. Seasonal comparison between cloud fractions in the northern and southern tropics (15.5N to 15.5S) in year 2010. Bars represent monthly averages (mean  SD) of 1 x 1 sector squares between 179.5W and 179.5E (n=5760 per bar). Data were obtained from the publicly available Aqua/MODIS satellite data set curated by NASA’s Earth Observatory (https://earthobservatory.nasa.gov/global-maps/MODAL2_M_CLD_FR). WIN, SPR, SUM and AUT refer to winter, spring, summer and autumn, respectively. WIN1 represents December for the northern latitudes and June for the southern latitudes. Asterisks indicate instances where the non-paired t-test identified significantly different means at level p <0.01. -- Table S1. Slope, correlation, 95% confidence intervals and p-values determined as part of the pairwise correlation analysis with the variables sea surface temperature, Chl-a and Kd(PAR), as well as aCDOM, ap and Kd(λ) at wavelengths 305, 313 and 320 nm. For Chl-a, aCDOM and ap, depth-weighted (3 m to 20% PAR depth) average values were used for the analysis., Peer reviewed

10 pages. -- Figures and tables. -- Figure S1: SEM image of (a) bulk g-C3N4 and (b) ultrathin g-C3N4, (c) N2 adsorption-desorption isotherms of bCN and uCN. -- Figure S2: FTIR spectra of OAC, OLMA and TiO2 before and after ligands remove. -- Figure S3: Zeta potential distribution spectrum of TiO2 after ligands removal (a) and uCN (b). -- Figure S4: SEM image and EDS compositional maps of a T1/uCN1 composite. -- Figure S5: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S6: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S7: SEM image of T1/uCN2 and corresponding EDS spectrum; Figure S8: Chromatogram plots for 0.5 ml of standard hydrogen injected every half hour. -- Table S1: Gas Chromatography Peak Processing Data based on figure S8. -- Figure S9: Standard hydrogen curve for gas chromatography. -- Table S2: Exponential decay-fitted parameters of fluorescence lifetime of uCN, TiO2 and T1/uCN1. -- Figure S10: Photocatalytic hydrogen generation amount on bCN, TiO2 and T1/bCN1 during 4 h under simulated solar light irradiation; Table S3: Photocatalytic hydrogen production about TiO2/g-C3N4 based catalysts. -- Table S4: The AQE values with different incident light wavelengths for T1/uCN1. -- Figure S11: (a) Stability cycles of the T1/uCN1 for H2 evolution under simulated solar light irradiation; (b) TEM image of T1/uCN1 after 20 h photocatalytic H2 evolution reaction and (c) XRD pattern of T1/uCN1 before and after 20 h photocatalytic H2O2 evolution reaction., CN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCAProgramme / Generalitat de Catalunya., Peer reviewed

6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas., Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity., Peer reviewed

16 pages. -- Scheme S1. Scheme of the synthesis of Co-TAA. -- Figure S1. FT-IR spectra of the model compound Co-TAA, and Poly-TAA-Co powder. -- Figure S2. (a-b) HRTEM images and (c-d) STEM images of Poly-TAA-Co. -- Figure S3. EELS chemical composition maps from the red squared area of the STEM micrograph. Individual Co L2,3-edges at 779 eV (red), N K-edges at 401 eV (green), O K-edges at 532 eV (blue), and C-K edges at 284 eV (grey) and composites of Co-N, Co-O, Co-C, N-O and Co-N-C. -- Figure S4. (a)-(b) Survey, high resolution C1s XPS spectra of Poly-TAA powder, respectively. -- Figure S5. (a) Survey, high resolution C1s XPS spectra of Poly-TAA-Co powder, respectively. -- Figure S6 TGA analysis of Poly-TAA-Co under argon by heating to 600 ℃ at the rate of 5 ℃/min. -- Table S1. Co K-edge EXAFS fitting parameters for Poly-TAA-Co. -- Figure S7. N2 adsorption and desorption of Poly-TAA (a), Poly-TAA-Co (b) and Poly-TAA-Co-CNT (c), respectively. -- Figure S8. (a) Total current density of Poly-TAA-Co-CNT (7:3). (b) FE of CO and H2 at various potentials for Poly-TAA-Co-CNT (7:3). -- Figure S9 Current density for H2 production on Poly-TAA-Co-CNT(1:1) and Poly-TAA-Co-CNT(3:7). and. -- Figure S10. Nyquist plots of the electrochemical impedance spectroscopy (EIS) data of (a) Poly-TAA-Co, (b) Poly-TAA-Co-CNT(1:1) and Poly-TAA-Co-CNT(3:7) electrodes after the activation process. -- Figure S11. Linear sweep voltammetry (LSV) curves of (a) Poly-TAA-Co. -- Figure S12. FE of H2 at various potentials on Co-TAA-CNT(3:7), CoPc-CNT(3:7) and Poly-TAA-Co-CNT(3:7). -- Figure S13. XRD patterns of Poly-TAA-Co-CNT loaded on carbon paper before and after CO2RR. -- Figure S14 HAADF-STEM (a, c), BF-TEM (b, d) and HRTEM micrographs (c, e) of Poly-TAA-Co-CNT (3:7) sample (before and after CO2RR). -- Figure S15. EELS chemical composition maps obtained from the red squared area of the STEM micrograph. Individual Co L2,3-edges at 779 eV (red), N K-edges at 401 eV (green), O K-edges at 532 eV (blue), and C-K edges at 284 eV (grey) and composites of Co-N, Co-O, Co-C, N-O and Co-N-C. (Poly-TAA-Co-CNT (3:7), after electrocatalytic CO2RR). -- Figure S16. Calculated energy diagrams for CO2 to CO at －0.5 V conversion on CoPc and CoTAA molecule, respectively. -- Table S2. The comparison of electrochemical reduction of CO2 to CO for reported cobalt based electrocatalysts., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and ICN2 and IREC are funded by the CERCA Programme /Generalitat de Catalunya., Peer reviewed

8 pages. -- Figure S1. SEM images of CoMoP. -- Figure S2. (a) SEM image of Co–Mo MOFs. (b–c) SEM images and (d) EDX spectrum of CoMoP. -- Figure S3. (a) SEM image of Na2MoO4-ZIF-67. (b–c) SEM images and (d) EDX spectrum Mo–CoP. -- Figure S4. (a) SEM image of ZIF-67. (b–c) SEM images and (d) EDX spectrum CoP. -- Figure S5. (a–d) TEM image of CoMoP. -- Figure S6. (a–d) HAADF-STEM micrographs of CoMoP. -- Figure S7. EELS chemical composition maps obtained from the red squared area of the STEM mi-crograph. Individual Co L2,3-edges at 779 eV (red), Mo M4,5-edges at 230 eV (green), P L2,3-edges at 132 eV (blue), N K-edge at 401 eV (pink) and C K-edge at 284 eV (orange). -- Figure S8. (a) OER and (b) HER polarization curves of CoMoP with different Mo content in 1.0 M KOH. -- Figure S9. Cyclic voltammograms for (a) CoMoP; (b) Mo–CoP; (c) CoP and (d) RuO2 in the non-faradaic region of 1.12–1.22 V vs. RHE at various scan rates. -- Figure S10. (a–c) SEM image and d) EDX spectrum of CoMoP after long term OER stability test-ing. -- Figure S11. (a–c) SEM image and d) EDX spectrum of CoMoP after long term HER stability test-ing. -- Table S1. Comparison of OER performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution. -- Table S2. Comparison of HER performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution. -- Table S3. Comparison of OWS performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed

19 pages. -- Figure S1. Synthesis scheme of Ni-2D-SA. -- Figure S2. PXRD patterns of Ni-2D-SA (black) and Ni-2D-O-SA (red). -- Figure S3. FT-IR spectra of Ni-2D-SA and Ni-2D-O-SA. -- Figure S4. chemical shift of 13C SSNMR spectra of Ni-2D-SA and Ni-2D-O-SA. -- Figure S5 SEM images of: (a) Ni-2D-O-SA, (b) Ni-2D-O-SA-CNT, (c) Ni-2D-SA-CNT. -- Figure S6. (a)-(c) HAADF-STEM images of Ni-2D-O-SA displaying the presence of atomically dispersed nickel atoms. (d) HAADF-STEM image and EDS mapping. -- Figure S7. Fourier transformed Ni K-edge EXAFS spectra of Ni-SA plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S1. The Ni K-edge EXAFS fitting parameters of Ni-SA. R：bond length, CN: coordination number. -- Figure S8. Fourier transformed Ni K-edge EXAFS spectra of Ni-2D-SA plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S2. The Ni K-edge EXAFS fitting parameters of Ni-2D-SA. -- Table S3. The Ni K-edge EXAFS fitting parameters of Ni-2D-O-SA. -- Figure S9. Fourier transformed Ni K-edge EXAFS spectra of Ni-2D-O-SA after immersed in KHCO3 for three days plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S4. The Ni K-edge EXAFS fitting parameters of Ni-2D-O-SA-KHCO3. -- Figure S10. Pore size distribution of Ni-2D-SA and Ni-2D-O-SA powder, respectively. -- Figure S11. PXRD of Ni-2D-SA-CNT, Ni-2D-O-SA-CNT and CNT. -- Figure S12. HAADF-STEM image and EDS elemental mapping for Ni-2D-O-SA-CNT. -- Figure S13. Left panel: i-t curve on Ni-2D-O-SA-CNT at -0.9 V vs. RHE for 1h Right panel: Calibration curves for methanol (0.2 mM DMSO as internal standard). -- Figure S14. NMR spectrum of the catholyte after 1 hour of CO2 reduction on Ni-2D-O-SA-CNT. -- Figure S15. (a and b) Current densities of CO2RR for Ni-2D-O-SA-CNT and Ni-2D-SA-CNT at various potentials. (c and d) Product distribution of CO2RR for Ni-2D-O-SA-CNT and Ni-2D-SA-CNT at various potentials. -- Figure S16. (a,c) CV curves on Ni-2D-O-SA-CNT and Ni-2D-SA-CNT with different scan rates (5, 10, 20, 50, 100 mV s-1). (b, d) Current at open circuit potential (OCP) versus scan rates of different samples. The electrode area is 1 cm-2. -- Figure S17. Product distribution for Ni-2D-O-SA-CNT under Ar-saturated 0.1 M KHCO3 electrolyte at various potentials. -- Figure S18. NMR spectrum of the catholyte after 1 hour of CO2 reduction on Ni-2D-O-SA-CNT. -- Figure S19. NMR spectrum of the catholyte after 1 hour of electro-reduction under Ar environment on Ni-2D-O-SA-CNT. -- Figure S21. XPS spectra of Ni-2D-O-SA-CNT on carbon paper before and after 1 and 5 hours of CO2RR test. -- Figure S22. Product distribution of CO2RR for 2D-O-SA-CNT (without nickel) at various potential. -- Figure S23. Free-energy profiles of hydrogen evolution reaction (HER) on selected segments of Ni-2D-SA and Ni-2D-O-SA, respectively. -- Figure S24. The adsorption energy for intermediates (from CO to methanol) on selected segments of Ni-2D-SA and Ni-2D-O-SA, respectively. -- Figure S25. Free energy diagram of CO2 to CH3OH on selected segments of Ni-2D-O-SA. -- Table S5. Performance comparison of our catalysts and previous reported molecular based electrocatalysts for conversion of CO2 to methanol., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed