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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 https://spei.csic.es/, [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: https://spei.csic.es/, This work was not supported by any external funding., No

Cells above the diagonal show the r value; cells below the diagonal show the density distribution.(PDF), Peer reviewed

We report the development of a multimodal lanthanide vanadate system suitable as dual T1-T2 MRI contrast agent as well as a luminescent imaging probe in the near-infrared region, using Dy3+ and Gd3+ as T2 and T1 components, respectively, and Nd3+ as the luminescent center. The vanadate matrix was chosen to avoid the undesired solubility associated to previously reported fluoride-based contrast agents. With such aim, we first optimized the design of the MRI system by comparatively evaluating the magnetic relaxivities of two different architectures consisting of i) uniform NPs incorporating both paramagnetic cations in solid solution (single-phase NPs), and ii) core-shell NPs consisting of a DyVO4 core coated with a homogeneous GdVO4 shell (DyVO4@GdVO4). We found that, although both samples presented magnetic relaxivity properties that make them adequate for their use as dual T1-T2 contrast agents for magnetic resonance imaging, the core-shell architecture would be more suitable because of their higher magnetic relaxivity values. Secondly, to prepare the multimodal system, the GdVO4 layer of such optimal dual T1-T2 MRI probe was doped with Nd3+ cations. An inert YVO4 intermediate shell was also introduced between the cores and the outer layer aiming to avoid energy transfer from Nd3+ to Dy3+, which would cause luminescence quenching. These core-shell-shell nanoparticles showed magnetic relaxivity values similar to those of the core-shell system and an intense luminescence in the near-infrared region. Moreover, they were dispersible and chemically stable under conditions that mimic the physiological media, and they were nontoxic for cells. Therefore, such multimodal nanoparticles meet the main requirements for their use as a dual T1-T2 contrast agent for magnetic resonance imaging and as a probe for luminescent imaging in the near-infrared region., This publication is part of the I + D + I Grants PID2021-122328OB-I00 and PID2020-118448RB-C21, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. This work was supported as well by Junta de Andalucía under grant no. P20_00182, co-financed by EU FEDER funds. Grant PRE2019-090170 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future” is also acknowledged., F2 F2b_ FTIR spectrum of single-phase nanoparticles. F2c_ TGA curve of the single-phase nanoparticles. F2d_XRD pattern of the single-phase nanoparticles_ black. F2d_GdVO4 PDF_red. F2d_DyVO4 PDF_navy. F4 F4b_ XRD pattern of the core-shell nanoparticles_ black F4b_GdVO4 PDF_red. F4b_DyVO4 PDF_navy. F4c: FTIR spectrum of the core-shell nanoparticles. F4d: TGA curve of the core-shell nanoparticles. F6 F6_top_blue: The longitudinal (1/T1) relaxation rates at 1.44 T for different contents of single-phase nanoparticles. F6_top_red: The longitudinal (1/T1) relaxation rates at 1.44 T for different contents of core-shell nanoparticles. F6_bottom_blue: The transverse (1/T2) relaxation rates at 1.44 T for different contents of single-phase nanoparticles. F6_bottom_red: The transverse (1/T2) relaxation rates at 1.44 T for different contents of core-shell nanoparticles. F7 F7b_XRD pattern of the Dy@Y@Nd:Gd sample_black. F7b_DyVO4 PDF_grey. F7b_GdVO4 PDF_red F7b_YVO4 PDF_blue F7c_FTIR spectrum of the Dy@Y@Nd:Gd sample. F7d_TGA curve of the Dy@Y@Nd:Gd sample. F9 F9_blue: Relaxation rates (1/T1) measured at 1.44 T for different lanthanide (Dy+Gd+Nd) concentration in the Dy@Y@Nd:Gd sample. F9_red: Relaxation rates (1/T2) measured at 1.44 T for different lanthanide (Dy+Gd+Nd) concentration in the Dy@Y@Nd:Gd sample. F10 F10_top: Excitation spectrum for the Dy@Y@Nd:Gd sample. F10_ bottom: Emission spectrum for the Dy@Y@Nd:Gd sample. F11 F11_grey: DLS curve obtained for DyVO4@YVO4@Nd-doped GdVO4 nanoparticles suspensions in water. F11_red: DLS curve obtained for DyVO4@YVO4@Nd-doped GdVO4 nanoparticles suspensions in MES medium. F11_blue: DLS curve obtained for DyVO4@YVO4@Nd-doped GdVO4 nanoparticles suspensions in PBS medium. F11_green: DLS curve obtained for DyVO4@YVO4@Nd-doped GdVO4 nanoparticles suspensions in saline medium. F12 F12_Histogram_1 day: Histogram of Dy@Y@Nd:Gd nanoparticles after aging in PBS medium at 37 ºC during one day. F12_Histogram_21 days: Histogram of Dy@Y@Nd:Gd nanoparticles after aging in PBS medium at 37 ºC during 21 days. F12_Histogram_35 days: Histogram of Dy@Y@Nd:Gd nanoparticles after aging in PBS medium at 37 ºC during 35 days. F13 F13d_Analysis of the total number of cells per well exposed to increasing concentration of DyVO4@YVO4@Nd-doped GdVO4 nanoparticles. F13e_Dead cell percentage to increasing concentration of DyVO4@YVO4@Nd-doped GdVO4 nanoparticles. F13f_Mitochondrial activity of the cells to increasing concentration of DyVO4@YVO4@Nd-doped GdVO4 nanoparticles. FS1 FS1a_Size distribution histogram of the single-phase nanoparticles containing Gd and Dy as a solid solution. FS1b_Size distribution histogram of the DyVO4 nanoparticles coated with a GdVO4 shell. FS1c_Size distribution histogram of the DyVO4 nanoparticles coated with a first YVO4 shell and with a second shell consisting of GdVO4 doped with Nd3+. FS2 FS2b_Size distribution histogram for the DyVO4 nanoparticles functionalized with PAA used as cores. FS2c_ DLS curve in water suspension for the DyVO4 nanoparticles functionalized with PAA used as cores. FS2d_XRD pattern for the DyVO4 nanoparticles functionalized with PAA used as cores. FS2d_DyVO4_PDF FS2e_FTIR spectrum for the DyVO4 nanoparticles functionalized with PAA used as cores. FS2f_TGA curve for the DyVO4 nanoparticles functionalized with PAA used as cores., Peer reviewed

The files contain the numerical data of the figures of the article "Influence of atomistic features in plasmon-exciton coupling and charge transfer driven by a single molecule in a metallic nano cavity". Journal: The Journal of Chemical Physics. The data files are organized in different folders and subfolders corresponding to each of the figures in the paper and the supplementary material. The details of the files and the information contained in each folder are explained in a particular README.txt file within the folder. [Description of methods used for collection/generation of data] Explained in the README files in the folder., The dataset includes the numerical data presented in J. Chem Phys. 161 (2024); "Influence of atomistic features in plasmon-exciton coupling and charge transfer driven by a single molecule in a metallic nano cavity", written by Bruno Candelas, Nerea Zabala, Peter Koval, Anton Babaze, Daniel Sánchez-Portal, and Javier Aizpurua (doi: 10.1063/5.0216464). The dataset is organized into folders according to the explanation in the README file, and each folder contains .txt files of the data for each of the figures indicated on its name, together with README instructions on each case., MCIU/AEI/10.13039/501100011033 and “ERDF A way of making Europe", Grants PID2022-139579NB-I00 and PID2022-140845OB-C66; Basque Government (Projects No. IT 1526-22 and IT1569-22 for consolidated groups of the Basque University)., See the README in the folders, Peer reviewed

Table A1. Main forest types in Spain, based on Blanco-Castro (1997), Spanish Forest Inventory documents, and the Spanish Forest Map. The Spanish Forest Inventory plots were classified into one of these forest types when at least 50% of their plot basal area belonged to the dominant species of each forest type. In forest types dominated by several species, none of those species accounted for ≥50% of the plot basal area by themselves. Selected forest types for this study are indicated in bold., Table A2. Dry biomass equations for each tree species used in the study. Carbon content of each species was taken from Montero et al. (2005)., Table A3. Contribution of climatic variables (from CHELSA database; https://chelsa-climate.org/) to conduct the PCA (see Figure A1). Climatic variables are ordered by their coordinates in the first PCA dimension., Table A4. Number of Spanish Forest Inventory plots belonging to the selected forest types within each area for analyses regarding carbon storage and sequestration., Table A5. Number of plots from the Spanish Forest Inventory classified as planted and natural forests for each analyzed forest type. We used the third and fourth Spanish Forest Inventory datasets, because the second Spanish Forest Inventory followed a different method to identify planted plots. The forest types selected for subsequent analyses in planted vs. natural forests are marked in bold., Table A6. Pearson’s correlations (r) among predictors. Absolute values > 0.5 are shaded gray. The color of the cell indicates whether the predictor is a biotic (green), anthropogenic (red) or abiotic variable (blue)., Table A7. Summary of the Generalized Linear Mixed Models for analyses regarding the effect of forest origin (native vs. non-native), climate type (wet vs. dry climate), their interaction, and the selected environmental predictors (forest structure, abiotic factors, and tree cutting) on carbon sequestration increment due to growth of living trees and ingrowth (transition between juvenile trees to adults). Estimate values indicate the magnitude of the influence that predictor variables have on carbon storage and sequestration. Positive and negative effects are indicated with the symbols “+” and “-”, respectively. Estimates in bold indicate significant relationships (p < 0.05). SE refers to standard error. CEC refers to cation exchange capacity., Table A8. Summary of the Generalized Linear Mixed Models for analyses regarding the effect of forest origin (native vs. non-native), climate type (wet vs. dry climate), their interaction, and the selected environmental predictors (forest structure, abiotic factors, and tree cutting) on carbon sequestration decrement due to tree loss, which was further divided into tree loss by natural mortality and tree loss due to tree harvesting. Estimate values indicate the magnitude of the influence that predictor variables have on carbon storage and sequestration. Positive and negative effects are indicated with the symbols “+” and “-“, respectively. Estimates in bold indicate significant relationships (p < 0.05). SE refers to standard error. CEC refers to cation exchange capacity., Table A9. Summary of the Generalized Linear Mixed Models for analyses regarding the effect of tree plantation on carbon sequestration increment due to growth of living trees and ingrowth (transition between juvenile trees to adults). All forest types occurred in wet climate. We show the results regarding tree plantation, but we included other environmental variables related to forest structure (canopy cover, tree density, stand basal area), abiotic factors (slope, mean temperature, water availability, sand, coarse fragments, cation exchange capacity) and management (tree cutting) in the models. Positive and negative effects are indicated with the symbols “+” and “-“, respectively. Estimates in bold indicate significant relationships (p < 0.05). SE refers to standard error., Table A10. Summary of the Generalized Linear Mixed Models for analyses regarding the effect of tree plantation on carbon sequestration decrement due to tree loss, which was further divided into tree loss by natural mortality and tree loss due to tree harvesting. All forest types occurred in wet climate. We show the results regarding tree plantation, but we included other environmental variables related to forest structure (canopy cover, tree density, stand basal area) and abiotic factors (slope, mean temperature, water availability, sand, coarse fragments, cation exchange capacity) in the models. Positive and negative effects are indicated with the symbols “+” and “-“, respectively. Estimates in bold indicate significant relationships (p < 0.05). SE refers to standard error., Figure A1. A) Ordination diagram of the principal components analysis (PCA), based on the first two axes, for SFI plots. We used climatic variables from the CHELSA database (https://chelsa-climate.org/) to conduct the PCA. See Table A3 for variable description and contribution to the PCA analysis., Figure A2. Boxplots indicating A) mean annual temperature, B) water availability (annual precipitation minus potential evapotranspiration divided by potential evapotranspiration), C) temperature seasonality, and D) precipitation seasonality for each group of SFI plots. The lower and upper box edges refer to the Interquartile Distance (IQD), i.e., the 25th and 75th percentiles. The dots beyond the whiskers are outliers, i.e., > 1.5*IQD. The horizontal line is the median (50th percentile). The ends of the lower and upper whiskers are the largest and smallest value within 1.5 times interquartile range above 75th percentile and below 25th percentile, respectively., Figure A3. Predicted means of carbon sequestration for non-native and native forests in wet and dry climates. Carbon sequestration is divided into its components: (A) tree growth as the C gain due to the growth of living trees; (B) tree ingrowth as the C gain due to the transition from juveniles to adults (i.e., new trees with DBH ≥ 7.5 cm) in the 5-m radius subplot; and (C and D) tree loss as the C loss due to dead trees, which was further divided into C loss by natural mortality and C loss due to tree harvesting. Different letters indicate significant differences between categories after accounting for multiple-comparison (Bonferroni) correction. Error bars represent standard errors (SE)., Figure A4. Predicted means of carbon sequestration increment due to (A) growth of living trees and (B) ingrowth (transition between juvenile trees to adults), in wet and dry climate. Different letters indicate significant differences between forest types after accounting for multiple-comparison (Bonferroni) correction. Error bars represent standard errors (SE). Non-native and native forest types are indicated with blue and yellow colors, respectively. Note variation in Y-axes among bar plots. See Table A1 for forest type nomenclature., Figure A5. Predicted means of carbon sequestration decrement due to tree loss, which was further divided into (A) tree loss by natural mortality and (B) tree loss due to tree harvesting, in wet and dry climate. Different letters indicate significant differences between forest types. Error bars represent standard errors (SE). Non-native and native forest types are indicated with blue and yellow colors, respectively. Note variation in Y-axes among bar plots. See Table A1 for forest type nomenclature., Peer reviewed

Contents: Supplementary Text: Institutional views of research articles versus review articles Supplementary Text: Research foci of the authors Supplementary Text: Diversity and ecology of marine microbes Supplementary Text: Acid brines and habitability of Mars Supplementary Text: Oakes Ames (1874-1950) Supplementary Text: Inter- and trans-disciplinary scientists Supplementary Text: “The Concept of Mind” Supplementary Text: Microbial weeds versus plant- and animal weeds Supplementary Text: Towards an understanding of keystone microbes Supplementary Text: Cellular stress and toxicity are conceptually and mechanistically distinct Supplementary Text: Work that followed from a theory-based study of ethanol stress Supplementary Text: “Can machines think?” Supplementary Text: The surface of Mars Supplementary Text: A note on planetary protection Supplementary Text: Candidate Phyla Radiation Supplementary Text: Quantification of competitive interactions Supplementary Text: “The Child’s Vision of the World” Supplementary Text: “Training Spontaneity Through the Intellect” Supplementary Text: Early developments in artificial intelligence Supplementary Text: Attitudes of journals and funding bodies Supplementary Text: Daniel C. Dennett III Supplementary Text: Global soil health, and a post-human biosphere Supplementary References, Peer reviewed

Supplementary materials and methods Supplementary Figures S1–S11 Supplementary Tables S1 and S2 Supplementary Tables S3 and S4 are provided as separate Excel files, Peer reviewed