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

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

13 pages. -- The PDF file includes: Supporting text. -- Figs. S1 to S1. -- Legends for Movies S1 to S4., Self_organized_appendix.pdf, pnas.2216024120.sm01.mp4, pnas.2216024120.sm02.mp4, pnas.2216024120.sm03.mp4, pnas.2216024120.sm04.mp4, Peer reviewed

Open access. Please, contact corresponding author in case of doubts, Description: The Malaspina2010_chlorophyll.xlsx file contains the fluorimetric chlorophyll a data of the circunnavigation expedition Malaspina 2010, which took place between 14/12/2010 and 14/07/2011 on board the BIO Hespérides. The data presented here were obtained between 16/12/2010 and 11/07/2011. The date and position of the sampling stations are listed in the third sheet of the file. Methods: Water samples for fluorimetric chlorophyll a (Chl a) determination were collected from 3 m depth with a 30-liter Niskin bottle and from selected depths between 10 and 200 m with a Rosette of 24 10-liter Niskin bottles attached to a CTD probe. Chl a determination was carried out as described in Estrada et al. (2012). Briefly, between 200 and 500 cm3 of seawater were filtered through GF/F glass fiber filters that were subsequently frozen at -20°C and, after a minimum of 6 hours, introduced in vials with acetone 90% and left for 24 hours in the dark, at 4ºC. The Chl a concentration in the acetonic extracts was measured with a Turner Designs fluorimeter calibrated with a pure Chl a standard (Sigma-Aldrich); no phaeopigment correction was applied. Size-fractionated analyses of Chl a were performed for samples from surface, the 20% surface PAR and the deep chlorophyll maximum. The analyses were carried out by sequential filtration of a 500 cm3 of seawater through Poretics (polycarbonate) membrane filters of pore sizes 20 μm, 2 μm and 0.2 μm, which were subsequently treated as the GF/F ones (Estrada, 2012), This work was supported by Consolider-Ingenio 2010, CSD2008-00077 of the former Spanish Ministerio de Ciencia e Innovación (ES) (now www.ciencia.gob.es) and the Consejo Superior de Investigaciones Científicas (CSIC) (ES)., Authors; Methods; Station positions; leg 1; leg 2; Legs 3-4; leg 5; leg 6; leg 7; References, Peer reviewed

Description: The Malaspina2010_chlorophyll.xlsx file contains the inorganic nutrient concentration data of the circunnavigation expedition Malaspina 2010, which took place between 14/12/2010 and 14/07/2011 on board the BIO Hespérides. The date and position of the sampling stations are listed in the “Data” sheet of the file. Methods: Water samples for measurement of the concentration of nitrate, nitrite (nitrate + nitrite in leg 1), phosphate and silicate were collected from 3 m depth with a 30-liter Niskin bottle and from selected depths between 10 and 200 m with a Rosette of 24 10-liter Niskin bottles attached to a CTD probe. Inorganic nutrient concentrations were determined by means of a Skalar autoanalyzer, following the standard spectrophotometric procedures described in Grasshoff et al. (1999) and Blasco et al. (2012); in leg 1, nitrite was not determined separately and phosphate was measured using a manual method (Vidal et al. 2012), This work was supported by Consolider-Ingenio 2010, CSD2008-00077 of the former Spanish Ministerio de Ciencia e Innovación (ES) (now www.ciencia.gob.es) and the Consejo Superior de Investigaciones Científicas (CSIC) (ES), Peer reviewed

-Dataset-/n/n The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns/n/n-Audio Content-/n/nThe audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. /n/n-Annotations-/n/nEach of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using the reduced set of five syllables described in Table 1 of [1] and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). /n/n-Dataset organization-/n/nThe dataset has wav files and label files. The files are named as /n./nThe pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., NpID}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab/n/n-Availability of the Dataset-/n/n The annotations are publicly shared and available to all. The audio is from commercially available releases. It cannot be publicly shared but can be made available on request for non-commercial research purposes. In the future, the dataset would be available for viewing and download through an interface in Dunya (http://dunya.compmusic.upf.edu). Beijing Opera Percussion Pattern (BOPP) dataset is a collection of 133 audio percussion patterns covering five pattern classes. The dataset includes the audio and syllable level transcriptions for the patterns (non-time aligned). It is useful for percussion transcription and classification tasks. The patterns have been extracted from audio recordings of arias and labeled by a musicologist.