BUSCADOR RECOLECTA

15 pages. -- Includes: Fig. S1- Fig. S7. -- Table S1 - Table S4. -- References, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

7 pages. -- Figure S1. Overview of system (Heterogeneous Multi-Habitat Test System - HeMHAS) used in the present study. -- Table S1: Values (mean ± standard deviation) of fipronil and 2,4-D determined at each initial concentration tested in the chronic experiments for the insecticide and herbicide, respectively, in micrograms per liter (μg/L). < QL = less than the quantification limit. -- Table S2: Values (mean ± standard deviation) of fipronil and 2,4-D determined at each concentration tested in the avoidance experiments for the insecticide and herbicide, respectively, in micrograms per liter (μg/L). -- Table S3: Values (mean ± standard deviation) of fipronil and 2,4-D determined at each concentration tested in the colonization experiments for the insecticide and herbicide, respectively, in micrograms per liter (μg/L). -- Table S4: Values (mean ± standard deviation) of fipronil and 2,4-D, determined at each concentration tested in the colonization experiment for the herbicide and insecticide, respectively, in micrograms per liter (μg/L). -- Table S5a: Mixed-design ANOVA for the avoidance test with D. magna exposed to fipronil. Time was treated as a within-subjects factor (repeated measurements) and compartments were treated as between-subjects factor. -- Table S5b:Within-subjects effects with Greenhouse-Geisser correction for degrees of freedom. -- Table S5c:Between-subjects effects. -- Table S6a:Mixed-design ANOVA for the avoidance test with D. magna exposed to 2,4-D. -- Table S6b:Within-subjects effects with Greenhouse-Geisser correction for degrees of freedom. -- Table S6c:Between-subjects effects., Peer reviewed

10 pages. -- Further information about “Data and methodology”, Figures S1, S2, S3, S4 and S5, and Table S1., Peer reviewed

This repository has two Python codes in the .ipynb format. -- "Georeferencing", which was the method used for the georeferencing of each of the individual captures taken with the Micasense multispectral sensor RedEdge-MX equipped on the UAV based on the sensor's metadata. This code is a modification of https://github.com/micasense/imageprocessing. -- "Merging", which is the novel part of the code used to combine and calculate the average value of each of the UAV captures overlapped with each other., Download of the data available in the publisher platform., This repository contains a Python workflow that can be used to stitch individual UAV (drone) captures to generate an orthomosaic over aquatic areas. This code arises as a complement to the methodology developed by Windle & Silsbe (2021), and can be applied to any method that retrieves Rrs from RAW data of any multispectral sensor mounted on UAVs. In addition, the code has been prepared for its use with datasets obtained with any UAV sensor., Peer reviewed

5 pages. -- This PDF file includes: Figs. S1 to S2. -- Tables S1 to S2., Peer reviewed

46 pages. -- Figure S1. Map of the study area. -- Table S1. Project calendar. -- Table S2. List of participants in the horizon scanning per group. -- Table S3. List of removed species. -- Table S4. Arrival scoring criteria. -- Table S5. Establishment scoring criteria. -- Table S6. Impact scoring criteria. -- Table S7. Confidence level scoring criteria. -- Table S8. Illustration of the two components of reliability. -- Table S9. Functional groups and associated abbreviations. -- Table S10. Native geographical region for inland species and associated abbreviations. -- Table S11. Native geographical region for marine species and associated abbreviations. -- Table S12. Classification of potential pathways. -- Table S13. Results of the beta regression model. -- Figure S2. Confidence level shown by experts. -- Table S14. Results of consistency analysis. -- Figure S3. Relationship between Krippendorff’s α and Kendall’s coefficient of concordance (W). -- Table S15. Number of potentially invasive species by thematic group. -- Table S16. List of species with very high risk. -- Table S17. List of species with high risk. -- Table S18. List of species with moderate risk. -- Table S19. List of species with low risk. -- Table S20. Prioritized species of special interest in insular Spanish territories. -- References., Peer reviewed

Material suplementario para el artículo "LOS “COLGANTES-AMULETO” DE ORO CASTREÑOS EN FORMA DE PIEL EXTENDIDA: APORTACIONES PARA SU ESTUDIO", Revista Complutum nº 34, 2 (año 2023). Incluyendo tablas complementarias con resultados analíticos obtenidos con la técnica SEM-EDX sobre las piezas estudiadas., Análisis SEM-EDS del grupo de los colgantes-amuletos castreños de oro en forma de piel extendida (material suplementario), SEM-EDS analysis of the animal hide-shaped gold "amulet pendats" group from the castros culture (Suppl. Material), Peer reviewed

[Detailed methodology] We sampled the flower-visitor interactions in paired olive farms located in Andalusia. The surveys were carried out in 24 paired olive farms in 2020. On the one hand, each paired olive farm is constituted by an olive farm with low-intensive herb cover management and an olive farm with intensive herb cover management. The low-intensive management consists of the maintenance of herb cover during most of the year being only removed in late spring by mechanic mowing or cattle. Conversely, intensive management consists of removing the herb cover permanently by using pre-emergence and/ or post-emergence herbicides sometimes in combination with ploughing the ground several times a year. On the other hand, each paired farm in each locality is surrounded by the same landscape complexity. We classified each paired farm into three categories of landscape complexity (simple, intermediate, and complex) according to the proportion of semi-natural area estimated within a 2 km radius buffer around the centroid of each pair of farms. Surveys were conducted in multi-specific floral stands. We selected two 10 m2 multi-floral stands per olive farm located inside the olive orchard matrix. They were carried out monthly from March to June for 2020 (3 sampling rounds in total), matching the peak flowering period of the ground herb cover. Sampling consisted of recording the abundance and diversity of plant-floral visitor interactions in each floral patch for 15 minutes in the morning (until 13 h) and for 15 minutes in the afternoon (until 17 h) every sampling round., [Description of methods used for collection/generation of data] We sampled the flower-visitor interactions in paired olive farms located in Andalusia. The surveys were carried out in 24 paired olive farms in 2020. On the one hand, each paired olive farm is constituted by an olive farm with low-intensive herb cover management and an olive farm with intensive herb cover management. The low-intensive management consists of the maintenance of herb cover during most of the year being only removed in late spring by mechanic mowing or cattle. Conversely, intensive management consists of removing the herb cover permanently by using pre-emergence and/ or post-emergence herbicides sometimes in combination with ploughing the ground several times a year. On the other hand, each paired farm in each locality is surrounded by the same landscape complexity. We classified each paired farm in three categories of landscape complexity (simple, intermediate, and complex) according to the proportion of semi-natural area estimated within a 2 km radius buffer around the centroid of each pair of farms. Surveys were conducted in multi-specific floral stands. We selected two 10 m2 multi-floral stands per olive farm located inside the olive orchard matrix. They were carried out monthly from March to June for 2020 (3 sampling rounds in total), matching the peak flowering period of the ground herb cover. Sampling consisted of recording the abundance and diversity of plant-floral visitor interactions in each floral patch for 15 minutes in the morning (until 13 h) and for 15 minutes in the afternoon (until 17 h) every sampling round., Our aim was to assess the effect of agricultural management and landscape context of olive orchard on the topology of plant-flower visitor networks., Field work was funded by Proyecto RECOVECOS (PID2019-108332GB-I00, funded by MICIN/AEI/10.13039/501100011033) and OLIVARES VIVOS (LIFE14 NAT/ES/001094, European Commission). Insect determination, final compilation and data processing were further funded by FEDER SUMHAL Work Package 9. Task 9.3.2. Model impact of land use change - Sustainability for Mediterranean Hotspots in Andalusia integrating LifeWatch ERIC (SUMHAL, LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020; MICINN through European Regional Development Fund). CSIC is acknowledged for supporting Open Access publication., Please, see Metadata_ Plant_Floral_Visitor_interactions.csv file. Descripción del contenido de los ficheros: Plant_Floral_Visitor_Interaction_Data.csv [shows the summary of plant-floral visitor interactions per olive farm].-- DB_ Plant_Floral_Visitor_interactions_Sampling_Info.csv [file has information about the geographic location and samplings of each olive farm].-- Metadata_ Plant_Floral_Visitor_interactions.csv [records information about the meaning of columns in “Data_Plant-Insect_Interac.csv” and “Sampling_Info.csv”]., Peer reviewed

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[Material and methods] In this experiment six ground moss species were utilized. • Ptychostomum capillare (Hedw.) D.T. Holyoak & N. Pedersen [== Bryum capillare Hedw.] • Dicranum scoparium Hedw. • Homalothecium aureum H. Rob. • Hypnum cupressiforme Hedw. • Tortella squarrosa (Brid.) Limpr. [== Pleurochaete squarrosa (Brid.) Lindb.] • Syntrichia ruralis (Hedw.) F. Weber & D. Moh. For the moss stand inoculation, seedbed pots with synthetic wadding as the growth substrate were used. The experimental units were placed in three different locations along a 100-kilometer altitudinal gradient in Central Spain, specifically at: • Estación Biológica “El Ventorrillo” (VENT), Madrid, Spain (1482 meters above sea level - m.a.s.l.) • Universidad Autónoma de Madrid (UAM), Madrid, Spain (717 m.a.s.l.) • Finca Experimental “La Higueruela” (HIG), Toledo, Spain (455 m.a.s.l.) The pictures were captured using two Mapir Survey 2 cameras (https://www.mapir.camera/). One camera had an RGB sensor for visual wavelengths, while the other had a Red + NIR sensor for near-infrared. The manufacturer adjusted the focal distance to approximately 25 cm from the samples. Both cameras were equipped with a 16MP Sony Exmor IMX206 sensor, featuring a Bayer RGB pattern. The red absorption occurred at 660 nm, and near-infrared was captured at 850 nm. We employed a transparent plastic light diffuser box with an interior lining of white paper, such as filter paper. This setup served to even out the lighting and reduce the appearance of harsh shadows. Each image included the samples, a colour chart (specifically, the X-Rite ColorChecker® Classic) for calibration, as detailed in prior works (Ritchie et al., 2008; Fischer et al., 2012), and a millimetre scale for calculating the pixel-to-millimetre ratio. Moss samples were labelled in each hollow. The cameras saved the images in raw format and we converted it to tif format without compression using Darktable 2.6.1 software (https://www.darktable.org/) (‘Darktable’, 2019). Then we used ImageJ software (Schindelin et al., 2012) to align each pair of NIR-VIS images and to homogenize colour histograms for each VIS and NIR image series in each locality. The ImageJ scripts are provided by Photomoss protocol (Molina-Bustamante et al., 2022)., This dataset contains a set of images that display moss samples from six distinct species over seedling trays from a common garden experimental set-up. We grow these moss samples on a background of white synthetic material. The capture of the images was in both near-infrared and visible light wavelengths. We provide additional files to fit the main dataset objective: use it as a test platform for evaluating the PhotomossR package. In this repository, you will discover images taken in both the near-infrared and visible light spectrums. These images offer comprehensive data for testing different spectral indices using the PhotomossR package (Molina-Bustamante, DeCastro-Arrazola, Doherty, & Medina, 2022)., This work is part of the projects UNITED (CGL2016-78070-P, funded by Spanish Agencia Estatal de Investigación [AEI] and Fondo Europeo de Desarrollo Regional [FEDER] European Union) and SCENIC (PID2019-106840GA-C21 and PID2019-106840GA-C22, funded by Spanish Ministerio de Ciencia e Innovación [MCIN] and AEI), M. M-B was funded by the Comunidad de Madrid, and the European Social Fund that co-financed through the Youth Employment Operational Program and the Youth Employment Initiative (YEI) grant PEJ-2017-AI/AMB-6504., Contains 5 folders based on the locality and date of image capture. Within each locality folder, there are 4 subfolders (nir, vis, rois, mask) and a 'names.csv' file that provides label names for the samples. • "nir" folder with images in the infrared spectrum. Images named by the date of capture. • "vis" folder with images in the visible spectrum. Images named by the date of capture. • "rois" folder that includes roi files. Inside this folder, there are as many subfolders as images in “nir” and “vis” folders. Each subfolder containing the roi files corresponding to the samples we want to analyse in each of the images. • "mask" folder with binary images of moss samples. Files are numerically named.in the order PhotomossR will analyse it., No