BUSCADOR RECOLECTA

The newly developed Ecological Indicator Values for Europe (EIVE) 1.0, together with all source systems in a flexible, harmonised open access database. Supplementary material 2: The analysed 31 EIV systems with original and harmonised plant nomenclature and original and rescaled indicator values for M, N, R, L and T (*.xlsx). Supplementary material 3: Documentation of additions to and modifications of the taxonomic backbone from Euro+Med (2022) in EIVE 1.0 (*.xslx). Supplementary material 8: EIVE 1.0 indicator values for niche position and niche width of M, N, R, L and T (*.xlsx)., Peer reviewed

APPENDIX 1. THE NETLOGO MODEL, [Introduction] Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe are compared to those of the western United States and Canada because they differ in their geographies and histories., [Methods] Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America., [Results] In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation., [Discussion] The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western United States and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria., Peer reviewed

Table A1. The locations of the regions on the grid of cells (given as the x,y coordinates of a center cell), the radius of their representative circles, and the variables defining their climates (HQT – hot quarter temperature, HQP – hot quarter precipitation, SWE – snow water equivalent)., [Introduction] Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe are compared to those of the western United States and Canada because they differ in their geographies and histories., [Methods] Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America., [Results] In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation., [Discussion] The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western United States and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria., Peer reviewed

Appendix 2 Table A3. Sorenson similarity between pairs of the 23 mountain ranges of southern and central Europe (SACEU)., [Introduction] Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe are compared to those of the western United States and Canada because they differ in their geographies and histories., [Methods] Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America., [Results] In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation., [Discussion] The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western United States and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria., Peer reviewed

[EN] The dataset is comprised of 48 NetCDF files, each representing a distinct temporal scale spanning from 1 to 48 months. It can be accessed and manipulated using various software tools, including GIS applications like QGIS and ArcMap, specialized applications like Panoply (https://www.giss.nasa.gov/tools/panoply/), and dedicated libraries such as ncdf4, raster, or terra in R, as well as netCDF4 or xarray in Python, among others. Each NetCDF file contains a 3-dimensional matrix with dimensions of 720x360x1476. Land pixels are marked with the value 1.0x10^30, and occasionally, calculation errors may lead to NaN values. The dataset has been generated in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., [ES] El conjunto de datos se compone de 48 archivos NetCDF, cada uno representando una escala temporal distinta que abarca desde 1 hasta 48 meses. Se puede acceder y manipular estos archivos utilizando diversas herramientas de software, incluidas aplicaciones SIG como QGIS y ArcMap, aplicaciones especializadas como Panoply (https://www.giss.nasa.gov/tools/panoply/), y librerías específicas como ncdf4, raster o terra en R, así como netCDF4 o xarray en Python, entre otras. Cada archivo NetCDF contiene una matriz tridimensional con dimensiones de 720x360x1476. Los píxeles de tierra se identifican con el valor 1.0x10^30 y los errores de cálculo con valores NaN. El conjunto de datos se ha generado en R utilizando el paquete SPEI (http://cran.r-project.org/web/packages/SPEI)., [EN] The SPEI database (SPEIbase) provides a comprehensive global record of drought conditions. It offers a standardized measure of drought, the Standardized Precipitation-Evapotranspiration Index (SPEI), computed at multiple time scales ranging from monthly to 48 months. The SPEI index considers both precipitation and potential evapotranspiration (PET), estimated using the FAO-56 Penman-Monteith method. This approach provides a robust and accurate assessment of drought conditions across various regions and timeframes. SPEIbase is freely accessible and can be used to support a wide range of applications, including drought monitoring, risk assessment, and climate change studies. This is an update of the SPEIbase v2.9 (https://digital.csic.es/handle/10261/332007).-- What’s new in version 2.10: 1) Based on the CRU TS 4.08 dataset, spanning the period between January 1901 to December 2023. For more details on the SPEI visit http://sac.csic.es/spei For more details on the SPEI database visit http://sac.csic.es/spei, [ES] La base de datos SPEI (SPEIbase) ofrece un registro global completo de las condiciones de sequía. Proporciona una medida estandarizada de la sequía, el Índice Estandarizado de Precipitación-Evapotranspiración (SPEI), calculado a múltiples escalas temporales que van desde un mes hasta 48 meses. El índice SPEI considera tanto la precipitación como la evapotranspiración potencial (ETP), estimada utilizando el método FAO-56 Penman-Monteith. Este enfoque proporciona una evaluación sólida y precisa de las condiciones de sequía en diversas regiones y periodos de tiempo. SPEIbase es de libre acceso y se puede utilizar para respaldar una amplia gama de aplicaciones, incluido el monitoreo de sequías,la evaluación de riesgos y los estudios sobre el cambio climático. Esta es una actualización de la versión SPEIbase v2.9 (https://digital.csic.es/handle/10261/332007) basada en el conjunto de datos CRU TS 4.08, que abarca el período comprendido entre enero de 1901 y diciembre de 2023. Para obtener más información sobre la base de datos SPEIbase, consulte: http://sac.csic.es/spei, This work was not supported by any external funding., No

RCP4.5 (dark bars) and RCP8.5 (dark + light bars) under the assumption of limited dispersal. Different colors indicate different plant orders. (TIF), Peer reviewed

Global biodiversity is negatively affected by anthropogenic climate change. As species distributions shift due to increasing temperatures and precipitation fluctuations, many species face the risk of extinction. In this study, we explore the expected trend for plant species distributions in Central America and southern Mexico under two alternative Representative Concentration Pathways (RCPs) portraying moderate (RCP4.5) and severe (RCP8.5) increases in greenhouse gas emissions, combined with two species dispersal assumptions (limited and unlimited), for the 2061–2080 climate forecast. Using an ensemble approach employing three techniques to generate species distribution models, we classified 1924 plant species from the region’s (sub)tropical forests according to IUCN Red List categories. To infer the spatial and taxonomic distribution of species’ vulnerability under each scenario, we calculated the proportion of species in a threat category (Vulnerable, Endangered, Critically Endangered) at a pixel resolution of 30 arc seconds and by family. Our results show a high proportion (58–67%) of threatened species among the four experimental scenarios, with the highest proportion under RCP8.5 and limited dispersal. Threatened species were concentrated in montane areas and avoided lowland areas where conditions are likely to be increasingly inhospitable. Annual precipitation and diurnal temperature range were the main drivers of species’ relative vulnerability. Our approach identifies strategic montane areas and taxa of conservation concern that merit urgent inclusion in management plans to improve climatic resilience in the Mesoamerican biodiversity hotspot. Such information is necessary to develop policies that prioritize vulnerable elements and mitigate threats to biodiversity under climate change., Peer reviewed

Name, number of occurrences, and parameters used to evaluate the species distribution model of 1924 plant species evaluated in this study., Peer reviewed

Number of pixels gained or lost and the proportion of change under four experimental scenarios (RCP4.5/RCP8.5 and limited/unlimited dispersal) for 1924 plant species in Central America and southern Mexico., Peer reviewed

[Description of methods used for collection/generation of data] Sanger sequencing, Fluorescent Fragment Analysis., This dataset corresponds to a comprehensive study of exons 3 and 6 of the cancer susceptibility gene TP53. For this purpose, we have used a minigene with exons 2 to 9, where we introduced 31 internal microdeletions, one intron replacement and 134 variants of exons 3 and 6 and intron 6 by site-directed mutagenesis that were assayed in SKBR3 cells. We identified four SRE-rich regions essential for exon recognition. Four control ±1,2 and 130 single nucleotide variants (SNVs) located at the SRE-rich intervals were assayed. We found 13 putative SRE-disrupting variants that impaired recognition of exons 3 (△(E3): 10-17%) or 6 (△(E6): 8-26%)., EAV-S lab is supported by a grant from the Spanish Ministry of Science and Innovation, Plan Nacional de I+D+I 2013-2016, ISCIII (PI23/00047) co-funded by FEDER from Regional Development European Funds (European Union)., • Folder: Fragment_Analysis. Fluorescent Fragment Analysis: Sub-folder Microdeletions: 80 *.fsa files of fluorescent fragment analysis of RT-PCRs of microdeletions Sub-folder Variants: 331 *.fsa files of fluorescent fragment analysis of RT-PCRs of variants • Folder: Sequences. 176 *.ab1 files of cDNA sequences of the wild type and mutant minigenes. Sub-folder cDNA. Transcript Sequencing. Sub-folder Microdeletions: 49 *.ab1 files of transcripts generated by microdeletions. Sub-folder Variants: 206 *.ab1 files of transcripts generated by variants. Sub-folder: Minigenes. Sequence files of wild type and mutant constructs: 89 *.ab1 files. Sub-folder Microdeletions: 28 *.ab1 files. Sub-folder Variants: 135 *.ab1 files • Folder: WT. Fragment analysis and sequencing files of the wild type minigene. Sub-Folder: Fragment_Analysis: 3 *.fsa files of fluorescent fragment analysis of RT-PCRs of the wild type construct. Sub-Folder: Sequences: 4 *.ab1 files of RT-PCRs and the construct sequences of the wild type minigene., Peer reviewed