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Compressed file containing 7 archives: environmental and biological data from invaded and non-invaded areas (original dataset); environmental and biological data from invaded area (to be used for data analysis along with the R script); environmental and biological data from non-invaded area (to be used for data analysis along with the R script); physiological and biological traits of corixids and their categories (to be used for data analysis along with the R script); affinity values of species for each trait category (to be used for data analysis along with the R script), physiological and biological traits of corixids and their categories (original dataset); document with detailed archives description., Biological invasions have become one of the most important drivers of biodiversity loss and ecosystem change world-wide. However, it is still unclear how invasions may interact with local abiotic stressors, which are expected to increase as global change intensifies. Furthermore, we know little about the response to biological invasions of insects, despite their disproportionate contribution to global animal biodiversity. The aim of the present work is to investigate the impact of an invasive aquatic insect on the co-occurrence patterns of native species of insects along a salinity gradient, and determine which assembly rules are driving these patterns. First, we characterised the habitat specialisation and functional niches of each species from physiological and biological traits, respectively, and their degree of overlap. Second, we used field data to compare the co-occurrence patterns of native and invasive species in invaded and non-invaded areas of southern Iberia and northern Morocco. Finally, we tested if habitat filtering or niche differentiation assembly rules mediate their co-occurrence. In non-invaded areas, habitat filtering drives habitat segregation of species along the salinity gradient, with a lower contribution of niche differentiation. The presence of the invasive insect modifies the distribution and co-occurrence patterns of native species. In invaded areas, niche differentiation seems to be the main mechanism to avoid competition among the invasive and native species, enabling coexistence and resource partitioning. The combined study of functional niche similarity and abiotic stressor tolerance of invasive and native species can improve our understanding of the effects of invasive species along abiotic stress gradients. This approach may increase our capacity to predict the outcomes of biological invasion in a global change context., Peer reviewed

Raw data on intact seeds in shorebirds This file contains data of the faecal samples collected from the field in different locations with date, faeces sample mass, plant species name, and the number of seeds per taxon. This file was made with Microsoft Excel 2016 shorebird.xlsx, Shorebirds (Charadriiformes) undergo rapid migrations with potential for long-distance dispersal (LDD) of plants. We studied the frequency of endozoochory by shorebirds in different parts of Europe covering a broad latitudinal range and different seasons. We assessed whether plants dispersed conformed to morphological dispersal syndromes. A total of 409 excreta samples (271 faeces and 138 pellets) were collected from redshank (Tringa totanus), black-winged stilt (Himantopus himantopus), pied avocet (Recurvirostra avosetta), northern lapwing (Vanellus vanellus), Eurasian curlew (Numenius arquata) and black-tailed godwit (Limosa limosa) in south-west Spain, north-west England, southern Ireland and Iceland in 2005 and 2016, and intact seeds were extracted and identified. Godwits were sampled just before or after migratory movements between England and Iceland. The germinability of seeds was tested. Intact diaspores were recovered from all bird species and study areas, and were present in 13% of samples overall. Thirteen plant families were represented, including Charophyceae and 26 angiosperm taxa. Only four species had an "endozoochory syndrome". Four alien species were recorded. Ellenberg values classified three species as aquatic and 20 as terrestrial. Overall, 89% of seeds were from terrestrial plants, and 11% from aquatic plants. Average seed length was higher in redshank pellets than in their faeces. Six species were germinated, none of which had an endozoochory syndrome. Seeds were recorded during spring and autumn migration. Plant species recorded have broad latitudinal ranges consistent with LDD via shorebirds. Crucially, morphological syndromes do not adequately predict LDD potential, and more empirical work is required to identify which plants are dispersed by shorebirds. Incorporating endozoochory by shorebirds and other migratory waterbirds into plant distribution models would allow us to better understand the natural processes that facilitated colonization of oceanic islands, or to improve predictions of how plants will respond to climate change, or how alien species spread., Peer reviewed

The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.6 (https://digital.csic.es/handle/10261/202305).-- What’s new in version 2.7: 1) Based on the CRU TS 4.05 dataset, spanning the period between January 1901 to December 2020. Using TLMoments::PWM instead of lmomco::pwm.ub for calculating distribution parameters. For more details on the SPEI visit http://sac.csic.es/spei, No

[EN] SPI01 grid: plain text. 5219 rows (excluding the header) and 1261 columns (excluding the X and Y coordinates). SPI12 grid: plain text. 5219 rows (excluding the header) and 1250 columns (excluding the X and Y coordinates). Episode descriptive files: duration and intensity integral maps, SPI01 and SPI12 averages, and spatial propagation maps., [ES] Malla SPI01: texto plano. 5219 filas -descontando el encabezado- y 1261 columnas -descontando las coordenadas X e Y-. Malla SPI12: texto plano. 5219 filas -descontando el encabezado- y 1250 columnas -descontando las coordenadas X e Y-. Archivos descriptivos de los episodios: mapas integrales de duración e intensidad, promedios de SPI’1 y SPI12 y mapas de la propagación espacial., Open Data Commons Attribution (ODC-By 1.0)., [EN] The database consists of two files in .txt format with the precipitation anomaly grids (Standardized Precipitation Index) calculated at 1 and 12 months over the Spanish peninsular domain, covering the period 2015/12_2020/12. These have been calculated from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). In addition, a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =< -0.84) and affected area (>20 % of the grid area) is included. For each episode we include 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. 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 dos archivos en formato .txt con las mallas de anomalías de precipitación (Standardized Precipitation Index) calculadas a 1 y 12 meses sobre el dominio peninsular español, cubriendo el periodo 12/2015_12/2020. Estas han sido calculadas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). Además, 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. 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., Peer reviewed

[EN] It contains a collection of .csv files by country and a netCDF file. Last one need specific data analyse software. [ES] Contiene una colección de archivos .csv por país y un archivo netCDF. Este último necesita software de análisis de datos específico., [EN] This database includes the representative Standardized Precipitation Evapotranspiration Index (SPEI) series for the different countries of the world from 1901 at the time scales from 1 to 48 months. The data is based on the average precipitation and reference evapotranspiration series from the Climatic Research Unit (last version) for the different world countries., [ES] Esta base de datos incluye la serie representativa del Índice Estandarizado de Precipitación y Evapotranspiración (SPEI) para los diferentes países del mundo desde 1901 en las escalas de tiempo de 1 a 48 meses. Los datos se basan en las series de precipitación media y evapotranspiración de referencia de la Unidad de Investigación Climática (última versión) para los diferentes países del mundo., Peer reviewed

Datos geoespaciales del índice de vegetación de diferencia normalizado (NDVI) de alta resolución espacial (1.1 km) para la España peninsular y Baleares con una resolución temporal quincenal (1981-2015)., [EN] NDVISpainDatabase is a high-resolution (1.1 km) Normalized Difference Vegetation Index dataset for the peninsular Spain and the Balearic Islands. This dataset covers from 1981 to 2015 with a biweekly time resolution., [ES] NDVISpainDatabase es una base de datos del índice de vegetación de diferencia normalizado de alta resolución espacial (1.1 km) para la España peninsular y Baleares. La base de datos cubre el periodo 1981-2015 con una resolución temporal quincenal., Spanish Commission of Science and Technology and FEDER ECOHIDRO (1550/2015, funded by the Natural Parks-Ministry of Agriculture and Environment) by the research projects PCIN-2015-220, PCIN-2017-020, CGL2014-52135-C03-01, CGL2017-83866-C3-3-R and CGL2017-82216-R, Water Works 2014 co-funded call of the European Commission by the project IMDROFLOOD, Assessment of Cross(X) - sectoral climate Impacts and pathways for Sustainable transformation JPI Climate co-funded call of the European Commission by the project CROSSDRO, FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462) by the project INDECIS (which is part of ERA4CS, an ERA-NET initiated by JPI Climate), Peer reviewed

datos geoespaciales en rejilla, [EN] This database provides information about drought conditions in Spain from 1961 from different drought indices (SPEI, SPI, SPDI, SCPDSI, SCPHDI, SCWPLM, SCZINDEX) with a 1.1km spatial resolution and a weekly time resolution. The database is updated annually., [ES] Esta base de datos ofrece información histórica sobre las condiciones de sequía desde 1961, de acuerdo a diferentes índices de sequía (SPEI, SPI, SPDI, SCPDSI, SCPHDI, SCWPLM, SCZINDEX), a una resolución espacial de 1.1 km y una resolución temporal semanal. Esta información se actualiza anualmente, Spanish Commission of Science and Technology and FEDER by research projects PCIN-2015-220, CGL2014-52135-C03-01, CGL2014-52135-C03-02 and CGL2014-52135-C03-03, Water Works 2014 co-funded call of the European Commission by the project IMDROFLOOD, European ERA4CS Joint Call for Transnational Collaborative Research Projects by the project INDECIS, Peer reviewed

[EN] It contains a netCDF file which needs specific data analysis software. [ES] Contiene un fichero netCDF que necesita software de análisis de datos específico., [EN] The dataset offers near real-time information about drought conditions at the global scale, with a 1 degree spatial resolution and a monthly time resolution. SPEI time-scales between 1 and 48 months are provided from 1955. The dataset is updated during the first days of the following month based on the most reliable and updated sources of climatic data., [ES] El dataset proporciona información en tiempo cuasi real sobre las condiciones de sequía en el mundo a resolución temporal mensual y espacial de 1 grado. Proporcionan los valores del SPEI desde 1955 hasta la actualidad a las escalas temporales de 1 a 48 meses. El dataset se actualiza en los primeros días del mes siguiente en base a las fuentes de datos climáticos más fiables y actualizadas., Spanish Commission of Science and Technology and FEDER by the research projects CGL2011-24185, CGL2011-27574-CO2-02 and CGL2011-27536, LIFE programme of the European Commission by the proyect “Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)”, Comunidad de Trabajo de los Pirineos by the project CTTP1/12 “Creación de un modelo de alta resolución espacial para cuantificar la esquiabilidad y la afluencia turística en el Pirineo bajo distintos escenarios de cambio climático”, Peer reviewed

datos geoespaciales en rejilla de SPEI, [EN] This dataset provides near real-time information about drought conditions in regions where corn, wheat, barley, soybeans and cotton are grown with a 0.5 degrees spatial resolution and a weekly time resolution. SPEI time-scales between 0.5 and 48 months are provided from 1979., [ES] La dataset presenta información en tiempo cuasi real de las condiciones de sequía en las regiones del mundo donde se cultiva maíz, trigo, cebada, soja y algodón a resolución temporal semanal y espacial de 0.5 grado. Se proporcionan los valores de SPEI a escalas temporales de 0.5 a 48 meses desde 1979., Ministry of Science and Technology of Spain, Grant/Award Number: PCI2019-103631, Peer reviewed

datos geoespaciales en rejilla de SPEI, [EN] This dataset provides near real-time information about drought conditions in semi-arid regions of the world with a 0.5 degrees spatial resolution and a weekly time resolution. SPEI time-scales between 0.5 and 48 months are provided from 1979., [ES] Esta dataset presenta información en tiempo cuasi real de las condiciones de sequía en las regiones semiáridas del mundo a resolución temporal semanal y espacial de 0.5 grado. Se proporcionan los valores de SPEI a escalas temporales de 0.5 a 48 meses desde 1979., Ministry of Science and Technology of Spain, Grant/Award Number: PCI2019-103631, No