BUSCADOR RECOLECTA

Island colonists are often assumed to experience higher levels of phenotypic diversification than continental taxa. However, empirical evidence shows that exceptions to this "island effect" do exist. Here, we tested this pattern using a thoroughly sampled continent-island system, the genus Pristurus, a group of sphaerodactylid geckos distributed across continental Arabia and Africa and the Socotra Archipelago. We used a recently published phylogeny and an extensive morphological dataset to explore whether Socotran and continental taxa differ in their dynamics of phenotypic evolution. Moreover, we used habitat data to examine if ecological specialisation is correlated with morphological change, reconstructing ancestral habitat occupancy and comparing phenotypic disparity and trait evolution between habitats. We found heterogeneity in the outcome of the colonisation of the Socotra Archipelago. Namely, only one of the three events of colonisation has resulted in an increase in body size. However, in general, Socotran species do not present higher levels or rates of morphological diversification than continental groups. Instead, habitat specialisation provides a more nuanced insight into body size and shape evolution in Pristurus. In particular, the colonisation of ground habitats appears as the main driver of morphological change, producing the highest disparity and evolutionary rates. Additionally, arboreal species show very constrained body size and head proportions, suggesting ecologically driven morphological convergence. Our results reveal a determinant role of ecological mechanisms in morphological evolution and corroborate the complexity of ecomorphological dynamics in continental-island systems., Peer reviewed

[Methods] We gathered presence/absence and abundance data from published studies and reports to characterize avian assemblages. The survey method varied across studies, but the same method was used for each habitat within a particular region, making data comparable using appropriate models., We gathered presence/absence and abundance data from published studies and reports for 319 well-characterized assemblages spanning 50 cities from Africa, Australia, Europe, North America and South America. We focused on cities with comparable survey data within urban habitats and in nearby natural habitat. The final dataset contains almost 10,000 records for 1507bird species, 66 of which are introduced (non-native) in at least one study region. Species abundance per unit area or unit time were available for 269 assemblages from 42 cities, comprising 1353 species. Following Newbold et al. (2015), we used published habitat descriptions to classify the intensity of human use in urban habitats for each assemblage: (1) highly urbanised environments mainly contain densely packed buildings with vegetation scarce or absent; (2) moderately urbanised environments are residential areas with single-family houses and associated gardens; and (3) little-urbanised environments have few buildings and abundant vegetation (e.g. urban parks). The habitats outside the city were assigned to either natural vegetation or rural habitat based on the description of the habitat given in the source paper. Standardised survey methods were used across habitats within each region., Ministerio de Ciencia e Innovación, Award: CGL2013-47448-P. ANID-FONDECYT., Peer reviewed

[Methods] Analytical methods are described in the associated publication: Giménez-Grau, P., Felip, M., Zufiaurre, A., Pla-Rabès, S., Camarero, L. & Catalan, J. (2020) Homeostasis and non-linear changes in the stoichiometry of P limited planktonic communities. Ecosphere,11:e03249 [Usage Notes] Initial reference conditions in the lake water column were sampled during three dates. They are reported separately, but they should be amalgamated if used as initial experimental conditions. There are a few missing values across the biogeochemical data. Data from the enclosure water column and the sediment traps are reported in separated files., Planktonic communities are naturally subjected to episodic nutrient enrichments that may stress or redress the imbalances in limiting nutrients. Human-enhanced atmospheric nitrogen deposition has caused profound N:P imbalance in many remote oligotrophic lakes in which phosphorus has largely become limiting. These lakes offer an opportunity to investigate the planktonic community response to nutrient fluctuations in P-limited conditions. The ENEX experiment in Lake Redon (Pyrenees), performed during August 2013, aimed to investigate the structural and stoichiometric effects of pulse nutrient additions on P-limited planktonic communities. We performed P (PO43-), and N (NH4+ or NO3-) additions to the summer epilimnetic community of the ultraoligotrophic lake using self-filling ~100 L enclosures and analysed the response to varying P availability, N:P imbalance, and N source. The nutrient additions were gradients within the range of values seasonally found in the lake and other oligotrophic lakes of the Pyrenees, with a further P level typical of mesotrophic lakes to provided non-limiting conditions., Ministerio de Ciencia e Innovación, Award: CGL2010–19737. Ministerio de Ciencia e Innovación, Award: CGL2016–80124-C2-1-P. Generalitat de Catalunya, Award: 2017 SGR 910. Ministerio de Ciencia e Innovación, Award: FPI BES-2014-070196. Ministerio de Educación, Cultura y Deporte, Award: FPU AP2010-3596., Peer reviewed

[Methods] There are three types of data: - Detection data: 1-3 files per fish - Fish information data: includes all the relevant information for each individual, e.g. when it was tagged, transmitter type, etc... - Receiver array information: includes the location of the receivers [Usage Notes] Metadata of the dataset: Villegas Ríos, David et al. (2021), Inferring individual fate from aquatic acoustic telemetry data, Dryad, Dataset, https://doi.org/10.5061/dryad.2bvq83bn0 Detections data (all files starting with 9002 or 9004): Date and time: date and time of detection Receiver: name of the receiver Transmitter: ID of the transmitter Transmitter name: empty Transmitter serial: serial number of the transmitter Sensor value: depth measure Sensor units: units of the depth measure (meters) Station name: name of the station where the receiver is places Latitude: latitude of the station Longitude: longitude of the station fishinformation.csv: all the columns are self-explained except the following: MinDelaySec: minimum delay of the transmitter, in seconds. MaxDelaySec: maximum delay of the transmitter, in seconds. Release_lat_geo: release latitude, in geographic coordinates. Release_lon_geo: release longitude, in geographic coordinates. array_information.csv: all the columns are self-explained except the following: deploymentdatetime_timestamp: date of deployment of any particular receiver. recoverydatetime_timestamp: date of recovery of any particular receiver. lat_geo: latitude of the receiver location, in geographic coordinates. lon_geo: longitude of the receiver location, in geographic coordinates. lat_utm: latitude of the receiver location, in UTM. lon_utm: longitude of the receiver location, in UTM, Acoustic telemetry has become a popular means of obtaining individual behavioural data from a wide array of species in marine and freshwater systems. Fate information is crucial to understand important aspects of population dynamics such as mortality, predation or dispersal rates. Here we present a method to infer individual fate from acoustic telemetry arrays of receivers with overlapping detection ranges. Our method depends exclusively on information on animal movements and the characteristics and configuration of the telemetry equipment. By answering a limited number of simple questions, our method identifies six different fates: tagging mortality, natural mortality, fishing mortality, predation, dispersal and survival. Applying the method to a cod telemetry dataset, we were able to determine fate for 97% of the individuals. We validate the results using several external sources of information, such as recaptures from fishers and control fish with known fate. The method is readily applicable to a wide array of species with minimal adjustments, expanding the range of hypotheses that can be tested using telemetry data., H2020 Marie Skłodowska-Curie Actions, Award: 793627., Peer reviewed

[Methods] DATASETS: ABUN.Herba.csv: Plant community data were drawn from Atlas Florae Europaeae (AFE, Jalas & Suominen, 1964-1999). The distribution of flora is geographically described using equally-sized grid cells based on the Universal Transverse Mercator projection and the Military Grid Reference System. Each cell was assigned to a dominant habitat type based on the WWF Biomes of the World classification (Olson et al., 2001), which defines different ecoregions. We consider each cell in an ecoregion to represent a species aggregation. TRAIT.Herba.csv: Mean height values were obtained from the LEDA database (Kleyer et al., 2008) for as many species as there were available in the database. Most of the missing values were taken from (Ordonez et al., 2010), and some of them inferred using a MICE (Multivariate Imputation by Chained Equations) approach (Buuren & Groothuis-Oudshoorn, 2011). Based on plant growth forms, 2610 herbaceous species (aquatic, herbs, or graminoid) were considered in this dataset. MODIS_AET_mean.bil: Mean (annual) actual evapotranspiration maps were obtained from data estimated through remote sensing (Mu et al., 2011), which are publicly available in the MODIS project website (http://www.ntsg.umt.edu/project/modis/mod17.php). This data was put into a .bil format using QGIS. A map for European ecoregions is included in the file ecoregions.bil. CODE: Python code for replicability of the results is provided. randomize.py: performs randomization tests for each cell, by comparing with random samples taken from species in the corresponding ecoregion. It yields p-value distributions for each ecoregion, from which it is easy to compute height clustering indices. The variation of coexistence probabilities as function of competitive strengths can also be reproduced using the output file from this code. evapo_data.py: calculates ecoregional mean and std for actual evapotranspiration from MODIS data. Similar code can be used to obtain gross primary productivity averages. This code can be used to correlate AET with clustering indices and latitude. Note that this code can be easily adapted to obtain AET by cell instead of by ecoregion, using latitude and longitude defining each AFE cell., Patterns in macroecology are related to species occurrence across meaningful spatial and temporal scales. The dataset provided here reports species distribution data (presence-absence) for herbaceous plants across a number of European habitats (ecoregions). Species occurrence is accompanied by the corresponding plant's maximun stem height values. This dataset has been used to unveil patterns of herbaceous plant height clustering in mid-latitude European ecoregions. Presence-absence data for herbaceous plants were drawn from Atlas Florae Europaeae (Jalas & Suominen, 1964-1999). Associated to each species, a dominant habitat (ecoregion) was assigned according to the WWF Biomes of the World classification. Each herbaceous species in an ecoregion was characterized by its maximum stem height. Mean height values were obtained for different sources. In order to correlate clustering patterns with productivity measures, actual evapotranspiration (AET) data is also provided. AET maps were obtained from data estimated through remote sensing (Mu et al., 2011), which are publicly available in the MODIS project website (http://www.ntsg.umt.edu/project/modis/mod17.php). Plant distribution and trait data across Europe unveils a relation between plant height clustering and actual evapotranspiration. This clustering is most evident in mid-latitude ecoregions, where conditions for growth (reflected in actual evapotranspiration rates) are optimal. Away from this optimum, climate severity leads to non-significant height clustering in actual communities., Ministerio de Economia y Competitividad, Award: CGL2012-39964. Ministerio de Economia y Competitividad, Award: CGL2015-69043-P. Ministerio de Ciencia, Innovación y Universidades, Award: PGC2018-096577-B-I00. Banco Santander, Award: PR87/19-22582. Ministerio de Economia y Competitividad, Award: CGL2012-39964., Peer reviewed

We have compiled a comprehensive dataset of field observations of iodine speciation and size distribution in marine aerosol (1983-2018). Since the source of iodine is mainly marine, only ship-borne campaigns (16 cruises) and coastal or insular campaigns (12 ground-based stations) have been considered. Iodine speciation measurements are heterogeneous and do not always cover the same species or group of species. The data can be classified in five groups according to the iodine species reported and their size segregation in fine and coarse aerosol: (1) total iodine (TI) and total soluble iodine (TSI) in bulk aerosol, (2) TI size distribution, (3) TSI and soluble speciation size distribution, (4) soluble speciation in fine aerosol fraction only and (5) soluble speciation in bulk aerosol only. Soluble speciation consist of iodide (I-), iodate (IO3-) and soluble organic iodine (SOI). For some of the cruises where the size distribution of soluble iodine species was reported (7 cruises) there are also measurements of major ions (MI) available. MI observations include Na+, NH4+, Mg2+, Ca2+, K+, Cl-, NO3-, SO42-, oxalate (C2O4-2), Br- and methanesulfonate (CH3SO3-), and derived quantities such as non-sea-salt (nss) K+, Ca2+ and SO42-. The associated paper discusses some aspects of data treatment and questions related to analytical methods employed to determine iodine speciation., Financial Support: J. C. G. M. acknowledges financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and the Ramon y Cajal Program (RYC-2016-19570). A.S.-L. acknowledges financial support from the European Research Council Executive Agency under the European Union's Horizon 2020 Research and Innovation programme (Project 'ERC-2016-COG 726349 CLIMAHAL')., Peer reviewed

A collection of 1200 texts (292 173 tokens) about clinical trials studies and clinical trials announcements in Spanish: - 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO). - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos. Texts were annotated with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). 46 699 entities were annotated (13.98% are nested entities). 10% of the corpus was doubly annotated, and inter-annotator agreement (IAA) achieved a mean F-measure of 85.65% (±4.79, strict match) and a mean F-measure of 93.94% (±3.31, relaxed match)., European Commission: InterTalentum - Programme for Post-Doctoral Talent Attraction to CEI UAM+CSIC (713366), Peer reviewed

The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O3). The aim of the H2020 CAPTOR project was to raise public awareness in a project focused on citizen science. Each testbed was supported by an NGO in charge of deciding how to raise citizen awareness according to the needs of each country. The data presented here correspond to the raw data captured by the sensor nodes in the Spanish testbed using SGX Sensortech MICS 2614 metal-oxide sensors. The Spanish testbed consisted of the deployment of twenty-five nodes. Each sensor node included four SGX Sensortech MICS 2614 ozone sensors, one temperature sensor and one relative humidity sensor. Each node underwent a calibration process by co-locating the node at a reference station, followed by a deployment in a non-urban area in Catalonia, Spain. All nodes spent two to three weeks co-located at a reference station in Barcelona, Spain (urban area), followed by two to three weeks co-located at three non-urban reference stations near the final deployment site. The nodes were then deployed in volunteers' homes for about two months and, finally, the nodes were co-located again at the non-urban reference stations for two weeks. All data presented in this repository are raw data taken by the sensors that can be used for scientific purposes such as calibration studies using machine learning algorithms, or once the concentration values of the nodes are obtained, they can be used to create tropospheric ozone pollution maps with heterogeneous sources (reference stations and low-cost sensors)., Peer reviewed

The file contains the raw occurrence dataset on lipophilic toxins as extracted from EFSA DWH on 9 June 2020 on 16,845 analytical results presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields)., Table A1 Description of FoodEx2 codes used to describe scallop species and their anatomical parts Table A2 Data cleaning steps applied to occurrence data on domoic acid in scallops Table A3 Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg) Table A4 Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample) Table A5 Descriptive statistics of body tissue weights (g) of scallops as submitted by data providers, Peer reviewed