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This Excel file includes the data and tracked calculations of the manuscript entitled "Sponge contribution to the silicon cycle of a diatom-rich shallow bay". It includes 7 spreadsheets with the following contents: - READ ME - Standing STOCK living sponges - Sponge Si consumption FLUX - Si RESERVOIR in sediments - Sponge Si FLUXES in sediments - DIATOM Si fluxes&stocks (Fig.5) - Calculations for discussion, This research was supported by: - the Spanish Ministry grants CTM2015-67221-R and MICIU: #PID2019-108627RB-I00 to Manuel Maldonado - the grant 12735 – AO2020 of the French National research program EC2CO to Jacques Grall - the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), co-funded by a grant from the French government under the program "Investissements d'Avenir", and the “Xunta de Galicia” postdoctoral grant IN606B-2019/002 to María López-Acosta., Peer reviewed

This Excel file includes the metadata of the survey of the predation activity of the nudibranch Doris verrucosa on the sponge Hymeniacidon perlevis, This research was supported by: - the grant 12735 – AO2020 of the French National research program EC2CO - the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), co-funded by a grant from the French government under the program "Investissements d'Avenir", and the “Xunta de Galicia” postdoctoral grant IN606B-2019/002, No

[Description of methods used for collection/generation of data] In both stations a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇T), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Periodically water samplings for dissolved oxygen (DO), pH in total scale at 25 ∘C (pH𝑇25) and total alkalinity (TA) were obtained during the sensor maintenance campaigns. DO and (pH𝑇25) samples were collected in order to validate the data obtained by the sensors. DO concentrations were evaluated with the Winkler method modified by Benson and Krause by potentiometric titration with a Metrohm 808 Titrando with a accuracy of the method of ± 2.9 μmol kg−1μmol kg−1 and with an obtained standard deviation from the sensors data and the water samples collected of ± 5.9 μmol kg−1μmol kg−1. pH𝑇25T25 data was obtained by the spectrophotometric method with a Shimadzu UV-2501 spectrophotometer containing a 25 ∘C-thermostated cells with unpurified m-cresol purple as indicator following the methodology established by Clayton and Byrne by using Certified Reference Material (CRM Batch #176 supplied by Prof. Andrew Dickson, Scripps Institution of Oceanography, La Jolla, CA, USA). The accuracy obtained from the CRM Batch was of ± 0.0051 pH units and the precision of the method of ± 0.0034 pH units. The mean difference between the SAMI-pH and discrete samples was of 0.0017 pH units., Funding for this work was provided by the projects RTI2018-095441-B-C21 (SuMaEco) and, the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS., Peer reviewed

[Description of methods used for collection/generation of data] Data was acquired in both stations using a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Once data (available at https://doi.org/XXX/DigitalCSIC/XXX) was validated, several processing steps were performed to ensure an optimal training process for the neural network models. First, all the data of the time series were re-sampled by averaging the data points obtaining a daily frequency. Afterwards, a standard feature-scaling procedure (min-max normalization) was applied to every feature (temperature, salinity and oxygen) and to pHT. Finally, we built our training and validations sets as tensors with dimensions (batchsize, windowsize, 𝑁features), where batchsize is the number of examples to train per iteration, windowsize is the number of past and future points considered and 𝑁features is the number of features used to predict the target series. Temperature values below 𝑇=12.5T=12.5 °C were discarded as they are considered outliers in sensor data outside the normal range in the study area. A BiDireccional Long Short-Term Memory (BD-LSTM) neural network was selected as the best architecture to reconstruct the pHT time series, with no signs of overfitting and achieving less than 1% error in both training and validation sets. Data corresponding to the Bay of Palma were used in the selection of the best neural network architecture. The code and data used to determine the best neural network architecture can be found in a GitHub repository mentioned in the context information., Funding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe", the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS (https://pti-waterios.csic.es/)., Peer reviewed

[EN] Egg size data for breeding Audouin's Gulls in the Ebro Delta during 1992-2021, with details on the age of the adults and clutch size., [ES] Datos de tamaño de huevo para la gaviota de Audouin nidificante en el Delta del Ebro durante 1992-2021, con detalles sobre la edad de los reproductores y tamaño de puesta., CIRIT (Generalitat de Catalunya, ACOM92/3051//10), ICONA, EU (LIFE projects (LIFE96/NAT/E/3118-B4-3200/96/502, LIFE NAT 2002/0502), Ministerio de Ciencia y Tecnología BOS2000-0569-C02-02, BOS2003-01960, CGL2006-04325/BOS, CGL2013-42203-R, CGL2017-85210-P), EU (Programme Quality of Life and Management of Living Resources, QLRT-2000-00839)., Peer reviewed

[Description of methods used for collection/generation of data] Periodically water sampling for dissolved oxygen (DO) and total alkalinity (TA) was done during the sensor maintenance campaigns of BOATS. In two sites, monthly samples were collected from the same depth as the sensors of the BOATS stations, at 1m at the oceanographic buoy, and 4m in PN Cabrera. Samples were taken for dissolved methane (CH4), dissolved oxygen (DO), total alkalinity (TA), dissolved organic carbon (DOC), Chlorophyll a (Chl a), In both stations monthly discrete samples were collected, at 1 m in the Bay of Palma, Surface for Cap ses Salines and at 4 m depth in Cabrera. Samples were collected by submerging a hose connected to a pump at the sensor height of the BOATS stations in Cabrera and the Bay of Palma. At the third site, the lighthouse of Cap ses Salines, a bucket was used to obtain surface water samples., readme provides background information for xlsx datafiles., Methane (CH4) gas is the most important greenhouse gas (GHG) after carbon dioxide, with open ocean areas acting as discreet CH4 sources and coastal regions as intense but variable CH4 sources to the atmosphere. In this database we report measured CH4 concentrations and calculated air-sea fluxes in three sites of the coastal area of the Balearic Islands Archipelago (Western Mediterranean Basin). CH4 levels and related biogeochemical variables were measured in three coastal sampling sites between 2019 and 2021. CH4 concentrations in seawater ranged from 2.7 to 10.9 nM, without significant differences between the sampling sites. Averaged estimated CH4 fluxes during the sampling period for the three stations oscillated between 0.2 and 9.7 μmol m−2 d−1 following a seasonal pattern and in general all sites behaved as weak CH4 sources throughout the sampling period., Funding for this work was provided by the projects RTI2018-095441-B-C21 (SuMaEco) and, the BBVA Foundation project Posi-COIN and the Balearic Islands Government project SEPPO (PRD2018/18). This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI OCEANS+., Peer reviewed

5 pages. -- Generating the simulated datasets. -- Testing the function. -- Additional tests. -- Fig. S1. Simulated datasets for 10 individuals., Peer reviewed

24 pages. -- Table S1: R functions developed specifically for this paper to test the effects of individual site fidelity, temporal and spatial variability in tracking data. The use of the functions and their arguments are described in detail. -- Table S2: summary of logger and deployment details for each colony, prior publication of the data used for this study, and permits and licences granted for the corresponding fieldwork. -- Table S3: number of individuals tracked repeatedly for each colony. -- Table S4: number of tracks per year and colony in the dataset used to run the year effect test (no repeated individuals). -- Table S5: population estimates from all breeding colonies of Cory’s, Scopoli’s and Cape Verde shearwaters available in the publisher literature. n refers to the number of tracks obtained from each colony. -- Figure S1: significant relationship between species inclusiveness values and longitude (top) and population size (breeding pairs) in the log scale (bottom) for Scopoli’s shearwater. -- Figure S2: schematic image of the main currents exploited by Cory’s and Scopoli’s shearwaters around the African continent., Peer reviewed

A tabular spreadsheet with the species, colony, unique identifiers for each bird and trip, latitude, longitude, year of tracking (1st year, 2nd year, etc.), and an ordering column that allows the positions to be ordered to form a track., on-breeding locations of Cory’s shearwaters (Calonectris borealis), Scopoli’s shearwaters (C. diomedea), and Cabo Verde shearwaters (C. edwardsii) tracked from the colonies of Berlenga, Chafarinas, Corvo, Faial, Graciosa, Montaña Clara, Pico, Selvagem, Sisargas, Terreros, Timanfaya, Veneguera, and Vila for Cory’s shearwaters; Cala Morell, Chafarinas, Filfla, Frioul, Giraglia, Gozo, Lavezzi, Linosa, Malta, Na Foradada, Na Pobra, Palomas, Pantaleu, Paximada, Porquerolles, Riou, Strofades, Tremiti, and Zembra for Scopoli’s shearwaters; and Curral Velho and Raso for Cabo Verde’s shearwaters. Animals were tracked between the years of 2006 and 2016, and data includes species, colony, unique identifiers for each bird and trip, latitude, longitude, year of tracking (1st year, 2nd year, etc.), and an ordering column that allows the positions to be ordered to form a track., Peer reviewed

8 pages. -- Table S1. Origin, team members, geographical location and year of the biometric measurements used in this study. [Origen, equipo, ubicación geográfica y año de las medidas biométricas utilizadas en este estudio.]. -- Table S2. The number of study colonies and adult Yellow-legged gulls measured per colony. [Número de colonias de estudio y gaviotas patiamarillas adultas medidas por colonia.]. -- Table S3. Means and standard deviations (SD) of all study variables for males and females from both regions. CA: Cantabrian-Atlantic; ME: Mediterranean. All measurements in mm. Sample sizes available for each variable in Figure 1. [Media y desviación estándar (DE) de todas las variables de estudio para machos y hembras de ambas regiones. CA: Cantábrico-Atlántico; ME: Mediterráneo. Todas las medidas en mm. Tamaños de muestra disponibles para cada variable en la Figura 1.]. -- Table S4. Results of the analysis of normality (Shapiro tests) for all study variables. CA: Cantabrian-Atlantic; MED: Mediterranean. [Resultados del análisis de normalidad (pruebas de Shapiro) para todas las variables de estudio. CA: Cantábrico-Atlántico; MED: Mediterráneo.]. -- Table S5. Results of the analysis of the homogeneity of the variances for all variables. CA: Cantabrian-Atlantic; MED: Mediterranean. [Resultados del análisis de homogeneidad de las varianzas para todas las variables. CA: Cantábrico-Atlántico; MED: Mediterráneo.]. -- Table S6. Correlation matrices (Pearson’s r) among study variables for males and females from both study regions. X indicates an estimation error in the correlation test due to the lack of sufficient finite observations. The strongest correlations are highlighted in bold. [Matrices de correlación (r de Pearson) entre variables de estudio para machos y hembras de ambas regiones de estudio. X indica un error de estimación en la prueba de correlación debido a la falta de suficientes observaciones finitas. Las correlaciones más fuertes se destacan en negrita.], Peer reviewed