BUSCADOR RECOLECTA

Table S1: Results of the literature survey to study WNV predominance over other Flavivirus spp.; Table S2: Summary of mosquito captures in the study sites; Table S3: Detailed bELISA and rRT-PCR findings throughout bird Family, study site and area; Table S4: Bird abundance indices per site and area throughout bird Order and Family. References [43,44,45,46,47,48,49,50] are cited in the Supplementary Materials., Peer reviewed

Additional file 1. Figure 1. Representing images of (A) a Control TUNEL-positive sheep oocyte stained with (a) Hoechst 33342 and (b) fluorescein; and (B) a Control TUNEL-negative sheep oocyte stained with (a) Hoechst 33342 and (b) fluorescein., Additional file 2. Table 1. Details of primers used for qPCR., Additional file 3. Figure 2. Depicted images of the cumulus cell populations in terms of (A) viability, apoptosis, and necrosis, (B) active mitochondria, and (C) intracellular levels of ROS and GSH using flow cytometry and analyzed in IDEAS software., Additional file 4. Figure 3. Depicted images of (A) a Control TUNEL-positive expanded sheep blastocyst stained with (a) Hoechst 33342 and (b) fluorescein; and (B) a Control TUNEL-negative expanded sheep blastocyst stained with (a) Hoechst 33342 and (b) fluorescein., Peer reviewed

Datasets and Rcodes employed in "A comparative study of resource selection functions and imperfect detection models with which to describe wildlife habitat selection: biologging vs. camera traps as data sources.", Peer reviewed

Raw data available for reviewers., Peer reviewed

[Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project] H2020 EuroSea project: The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: https://eurosea.eu/). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626). Task 2.3: Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report Barceló-Llull et al. (2020) and the complete analysis is available here Barceló-Llull et al. (2022). Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France). Subtask 2.3.1: Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT. Where? The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase. [Report with the full analysis] The complete analysis can be found in this report: Barceló-Llull et al. (2022). [Codes for the analysis] The codes generated to develop Subtask 2.3.1 can be found on GitHub: https://github.com/bbarcelollull/EuroSea_subTask_2.3.1, The dataset includes: 1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. 2) Simulated configurations (or sampling strategies) in each region (PKL file format). 3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). 4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)). 5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format)., EuroSea – Improving and Integrating European Ocean Observing and Forecasting Systems for Sustainable use of the Oceans 862626. European Commission., Peer reviewed

MMC S1. Additional STM images relevant for this article are included in the Supplementary material., Peer reviewed

Supplementary List of References (cited in Table I)., Peer reviewed

Supplementary Information Fig. 1: Activity/depth profiles for 210Pb excess (blue diamonds) and 214Pb (orange squares) as a proxy for 210Pb supported activity for the upper and lower salt marshes studied.-- Supplementary Information Table 1. Mean (SE) of isotopic nitrogen and carbon values (‰) of salt marsh plant species and surficial sediments (2cm depth) collected at the eleven study sites. N indicates the number of samples analysed; it is indicated also when different sample numbers were analysed for Nitrogen (N) and Carbon (C). NM means no material found in the sampling units.-- Supplementary Information Table 2. Mean, quantiles (50, 2.5 and 97.5%) and SD of proportions of each carbon source for the surficial sediments (2cm depth) of each sampling site and according to plant species dominant of the salt marsh. POM is particulate organic matter. Asterisk (*) indicates the major median source contribution value when differentiate from the other sources. The quantiles 2.5 and 97.5% are Bayesian credible intervals. Dash (-) indicates organic matter source or dominant plant species no present in the site.-- Supplementary Information Table 3. Carbon and nitrogen isotopic signatures of organic matter allochthonous sources from the literature. POM: particulate organic matter.-- Supplementary Information Table 4. Summary of potential links between dependent and predictive variables incorporated in the SEM analysis and the rationale behind the potential link. Links predicted as positive are indicated with a blue dot, negative with red and neutral with black. OC: organic carbon, OM: organic matter, DOC: dissolved organic carbon, SAR: sediment accretion rate, C:N: carbon and nitrogen ratio. Interpretations for mean grain size are based on the So (sorting coefficient, with larger numbers for larger grains).-- Supplementary References., Peer reviewed

Organic carbon stocks, salt marsh plant biomass, crab burrow abundances and diameters. This data was generated by sampling at 11 salt marsh sites along 3000 km of coastal line in southern SW Atlantic coast in South America. Organic carbon stocks and burial rates extarcted from other publications and used to update global estimates are also included with their respective references. Values of biotic and abiotic drivers included in the Structural Equation Model (SEM) to evaluate their roles in belowground organic carbon stocks., Peer reviewed

Supplemental material includes a detailed information of the DFT calculations, a list of Stevens operators and the second quantization expression of the basis states, together with the derivation of the rate equation., Peer reviewed