Resultados de investigación

Table S1: Reactive epitope data analysis., Peer reviewed

Figure S1: Surface Ag 2 alignment in A. marginale, Ehrlichia chaffeensis, Rickettsia spp., Wolbachia spp with Peptide 2 and Peptide 3. Figure S2: Entire Western blots including the intensity ratio of each band that was relevant in the study., Peer reviewed

8 pages. -- The supporting data contains: Text S1. CIP, SULF and TRIM concentration analysis procedure in exposure water. -- Text S2. RT-qPCR analysis. -- Table S2. Specific primers of target genes in gilthead seabream used for qRT-PCR.. -- Table S3. Mean +/- SD effective concentrations (μg L−1) on the different sampling days in the exposure tanks. Last column indicates the average +/- SD concentration (μg L−1) throughout the whole experiment. -- Table S4. Plasma parameters of sea bream, Sparus aurata exposed to CIP, SULF and TRIM for 21 days. (n=5) for each treatment., Peer reviewed

10 pages. -- List of contents: Supplementary text S1. Measuring the refined SEEA EA environmental income. -- Supplementary text S2. AAS and rSEEA environmental income comparison. -- Supplementary text S3. Measuring the standard SNA final products and values added in the pine-forest-farm case studies., Peer reviewed

[EN] 1. Compressed folder ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip'): 40 folders with the graphic description of each event, numbered from 1 to 40 and with their respective start and end dates (e.g. '19_1973_oct-1974_feb'). In each of them, three folders: 'maps' with the integral maps of duration and intensity (.png format), 'seq_ano' with the maps of the spatial spread (.png format) and 'ts' with the averages of SPI01 and SPI12 and the area affected by the drought (SPI12 =< -0.84) (.png format). Map legends (.png format). .xlsx file showing the identification and characterization of the events. 2. Compressed folder ('Drought_events_characterization_code.zip'): project, script and folder with the data necessary to generate the descriptive analysis material in R. 3. netCDF file ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc'). Variables (6): 'SPI-01' (1-month Standardized Precipitation Index), 'SPI-03' (3-month Standardized Precipitation Index), 'SPI-06' (6-month Standardized Precipitation Index), 'SPI-12' (12-month Standardized Precipitation Index), 'SPI-36' (36-month Standardized Precipitation Index) and 'evnt' (Drought episodes). Temporal extent: 1272 (months from 1915-01 to 2020-01). Spatial extent: longitude = 125, latitude: 77, units = 0.1 degrees. Reference system: EPSG 4326, WGS84 projection, geographic coordinates. Open Data Commons Attribution (ODC-By 1.0)., [EN] The database consists of a netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') and two zipped folders ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' and 'Drought_events_characterization_code.zip'). The first folder includes a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =< -0.84) and area affected (>20 % of the grid area). For each episode, the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode are included. The second folder includes the code necessary to generate the descriptive analysis material. The netCDF consists of six variables. The first five are the precipitation anomalies (Standardized Precipitation Index at 1 month, 3, 6, 12 and 36 time scales: 'SPI-01', 'SPI-03', 'SPI-06', 'SPI-12' and 'SPI-36'), these have been obtained from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). The sixth variable is the drought events themselves ('evnt'), numbered from 1 to 40. These six variables refer to the Spanish peninsular territory and the time period 1915/01-2020/01. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By)., [ES] La base de datos consta de un netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') y dos carpetas comprimidas ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' y 'Drought_events_characterization_code.zip'). En la primera carpeta se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =< -0.84) y superficie afectada (>20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. En la segunda carpeta se incluye el código necesario para generar el material del análisis descriptivo. El netCDF consta de seis variables. Las cinco primeras son las anomalías de precipitación (Standardized Precipitation Index a escala temporal de 1 mes, 3, 6, 12 y 36: ‘SPI-01’, ‘SPI-03’, ‘SPI-06’, ‘SPI-12’ y ‘SPI-36’), estas han sido obtenidas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). La sexta variable son los eventos de sequía propiamente dichos (‘evnt’), numerados del 1 al 40. Estas seis variables están referidas al territorio peninsular español y el periodo temporal 1915/01-2020/01. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By)., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., No

Supplementary data 1: -Sheets: 20 -Tables: 10 -Figures: 1, Peer reviewed

9 pages. -- Figure S1: RGB (Red–Green–Blue) composite image on (a) 8, (b) 13, and (c) 18 August 2021 of the Sentinel-2 satellite (10 m spatial resolution). -- Figure S2: Turbidity (FNU) on (a) 3, (b) 8, (c) 13, and (d) 18, August 2021 of the Sentinel-2 satellite (10 m spatial resolution); (e–h) the same for chlorophyll-a concentration (Chl-a, mg/m3). -- Figure S3: Sentinel-2 and Landsat-8 RGB (Red–Green–Blue) composite image acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S4: Turbidity (FNU) from Sentinel-2 and Landsat-8 acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S5: Chlorophyll-a concentration (Chl-a, mg/m3) from Sentinel-2 and Landsat-8 acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S6: RGB (Red–Green–Blue) composite image from Sentinel-2 and Landsat-8 acquired on (a) 11, (b) 12, (c) 21, and (d) 28 March 2021. -- Figure S7: Turbidity (FNU) from Sentinel-2 and Landsat-8 acquired on (a) 11, (b) 12, (c) 21, and (d) 28 March 2021. -- Figure S8: (a) RGB (Red–Green–Blue) composite image, and (b) Turbidity (FNU) from Sentinel-2 on 21 March 2021 corresponding to the southeastern shore of Mar Menor., Peer reviewed

Supplementary material 1: Relationship between favorability and probability. Supplementary material 2: Testing the theory with empirical data., Peer reviewed

Supplement Table: All the observed species in wildlife-camera data during spring 2021 in Southern Finland in 18 study sites and 163 cameras., Peer reviewed

Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation., Peer reviewed