Resultados de investigación

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

Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Reporting Summary, Peer reviewed

Supplementary Data S1. Supplementary Raw Research Data. Supplementary Data S2. Supplementary Raw Research Data., Peer reviewed

Supplementary Data 1 Supplementary Data 2, Peer reviewed

Table S1. AcrB truncated variants identified in the H. influenzae strain collection under study. Table S2. AcrR truncated variants identified in the H. influenzae strain collection under study. Table S3. Summary of FtsI, ArcA, ArcB and ArcR variant distribution across H. influenzae strain collection., Peer reviewed

Peer reviewed

1. "ML_interactions_dna_barcoding_samples.csv" Data on the samples of bird droppings and mammal scats for which we successfully identified the bird species that dispersed the seeds through DNA-barcoding analysis. The dataset includes 3093 rows for 3063 samples because some samples (droppings or scats) included more than one seed species. That is, 3063 samples included 3093 interaction events between a frugivore-plant species pair, and contained a total of 14,683 seeds., Variables: sample_code: sample level codes.-- landscape: study landscape (seven levels).-- date: sampling date.-- seed_sp: seed species identified visually (see Table S4). In 11 out of the 3063 samples DNA barcoding also contributed to seed species identification.-- nseeds: number of seeds per plant species in the sample.-- habitat: habitat of seed deposition, where the samples were collected (two levels: ‘forest’ and ‘matrix’).-- mhabitat: microhabitat type of seed deposition, where the samples were collected (four levels: ‘tree’, ‘shrub’, ‘pylon’ and ‘open’).-- perch: perch of seed deposition (three levels: ‘canopy’, ‘pylon’ and ‘open’ for seed deposition in open spaces).-- perch_type: perch type of seed deposition (two levels: ‘natural’ and ‘pylon’ for anthropogenic perches).--sampling_type: sampling type for sample collection (three levels: ‘tray’ for sampling in seed traps, ‘transect’ for sampling within belt transects, and ‘observed’ for samples found visually outside seed traps and transects). disperser_group: group of seed disperser (two levels: ‘bird’ and ‘mammal’).-- disperser_sp: bird and mammal species identified through DNA barcoding, following taxonomy from the ‘Birds of the World’ (www.birdsoftheworld.org(opens in new window)) and the ‘Integrated Taxonomic Information System’ for mammals (www.itis.gov(opens in new window)).-- disperser_sp_original: bird and mammal species identified in BOLD or BLAST.-- identification: whether the seed disperser that ejected the sample (dropping or scat) was successfully identified through DNA barcoding (‘yes’: 3090 out of the 3093 samples) or was inferred (‘inferred’: 3 mammal scats) from DNA barcoding results of similar samples (i.e. mammal scats) from the same landscape.-- sequence: nucleotide sequence obtained through DNA barcoding analysis to identify the disperser species.--similarity: percentage of similarity with best matching sequence in BOLD or BLAST.-- genbank_anumber: accession number of best matching sequence deposited in GenBank; ‘Private’ denotes private sequences from BOLD, whereas codes starting with ‘BIN_Id_in_BOLD’ denote ‘Barcode Index Numbers’ from BOLD.-- similarity_best_match_genbank: percentage of similarity with best matching sequence in GenBank, provided when best matched sequences in BOLD were ‘Private’. top_genbank_anumber_if_private: accession number of best matching sequence deposited in GenBank when best matching sequence in BOLD were ‘Private’.-- plant_sequence: nucleotide sequence obtained through DNA barcoding analysis to identify the seed species., 2. "ML_networks_per_landscape_habitat.csv" Data on seed-dispersal networks between frugivore species and the seed species they dispersed in the forest and matrix of the study landscapes. The dataset includes 401 rows, that is, interactions between species pairs in each habitat type and study landscape (range = 44–83 pairwise interactions per landscape). The interaction weight of pairwise interactions was quantified as the seed-rain density (dispersed seeds per m2) of plant species i dispersed by frugivore species j in the forest and matrix of each study landscape. These data result from combining seed-rain data measured in seed traps and transects and the contribution of frugivore species to the seed rain of each plant species (extracted from ‘ML_interactions_dna_barcoding_samples.csv’) as explained in the Methods section and the SI Appendix (Appendix S1) of the Supporting Information., Variables: landscape: study landscape (seven levels).-- habitat: habitat of seed deposition, where the samples were collected (two levels: ‘forest’ and ‘matrix’).-- disperser_group: group of seed disperser (two levels: ‘bird’ and ‘mammal’).-- disperser_sp: bird and mammal frugivorous species that dispersed the seeds.-- seed_sp: dispersed seed species.-- ism2: interaction-level seed rain quantifying the weight of pairwise interactions as the seed-rain density (srij, expressed as seeds per m2) of plant species i dispersed by frugivore species j in the forest and matrix of each study landscape., 3. "ML_disperser_traits.csv" Species-level data on body mass and hand-wing index (HWI) for the 43 frugivore species (43 rows) identified as seed dispersers after DNA-barcoding analyses of bird droppings and mammal scats containing seeds. Taxonomy from the ‘Birds of the World’ (www.birdsoftheworld.org) and the ‘Integrated Taxonomic Information System’ for mammals (www.itis.gov(opens in new window)). Trait data was extracted from AVONET database (https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898(opens in new window)) for birds and from EltonTraits 1.0 (https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/13-1917.1) for mammals., Variables: disperser_sp: animal species.-- disperser_group: group of seed disperser (two levels: ‘bird’ and ‘mammal’).-- order: taxonomic Order.-- family: taxonomic Family.-- body_mass: mean body mass of the species in grams.-- HWI: hand-wing index of bird species expressed as percentages (HWI = 100 × Kipp’s distance / wing length). For mammals, HWI = NA.-- data_source: database from which the traits were extracted., 4. "ML_bird_migration.csv" Migration data for the 40 bird species that acted as seed dispersers (SI Appendix, Table S3) at the study landscapes (101 rows) because the migration traits vary geographically within species. The data were extracted from González-Varo et al. (2021; https://doi.org/10.1038/s41586-021-03665-2(opens in new window)), which collated data from multiple data sources (see this article for details; data including original sources available at https://doi.org/10.5061/dryad.15dv41nx3(opens in new window))., Variables: landscape: study landscape (seven levels).-- disperser_sp: bird species.-- prop_migrants: variable characterizing the proportion of migrants (Pmigrants) of the frugivorous bird species at the study landscapes: 0, non-migrant population; 0.1, only a minor fraction migrates; 0.25, a larger fraction migrates but non-migrants prevail; 0.5, roughly half of the population migrates; 0.75, migrants prevail; 0.9, only a minor fraction does not migrate; 1: the whole population migrates.-- migrant_type: type of migrant of the bird species in the study landscape (three levels: ‘non_migrant’ for resident birds; ‘short_distance’ for Palearctic migrants; ‘long_distance’ for Afro-Palearctic migrants)., 5. "ML_plant_traits.csv" Trait data for the 48 plant species (SI Appendix, Tables S4 and S5) at the different study landscapes (99 rows) because phenological data vary geographically within species. Mean seed weight and plant height were obtained at the plant species level, whereas phenological data was obtained for the plant species at the bioclimate of the study landscapes. The data sources for seed mass and plant height are detailed in SI Appendix, Table S5. Phenological data was obtained from González-Varo et al. (2021; https://doi.org/10.1038/s41586-021-03665-2(opens in new window))., Variables: landscape: study landscape (seven levels).-- seed_sp: seed species (see Table S4).-- seed_weight_mg: mean seed weight in milligrams.-- plant_height_m: mean plant height in meters.-- exotic_cultivated_planted: Bernoulli-distributed variable to classify the seed species according to the origin of their adult plants in each landscape (1: exotic or planted; 0: wild and native).-- fruit_L: The average start date (dstart) of the fruiting (seed-dispersal) period expressed on a monthly scale.-- fruit_R: The average end date (dend) of the fruiting (seed-dispersal) period expressed on a monthly scale., Seed dispersal by frugivores is a fundamental function for plant community dynamics in fragmented landscapes, where forest remnants are typically embedded in a matrix of anthropogenic habitats. Frugivores can mediate both connectivity among forest remnants and plant colonization of the matrix. However, it remains poorly understood how frugivore communities change from forest to matrix due to the loss or replacement of species with traits that are less advantageous in open habitats, and whether such changes ultimately influence the composition and traits of dispersed plants via species interactions. Here, we close this gap by using a unique dataset of seed-dispersal networks that were sampled in forest patches and adjacent matrix habitats of seven fragmented landscapes across Europe. We found a similar diversity of frugivores, plants and interactions contributing to seed dispersal in forest and matrix, but a high turnover (replacement) in all these components. The turnover of dispersed seeds was smaller than that of frugivore communities because different frugivore species provided complementary seed dispersal in forest and matrix. Importantly, the turnover involved functional changes towards larger and more mobile frugivores in the matrix, which dispersed taller, larger-seeded plants with later fruiting periods. Our study provides a trait-based understanding of frugivore-mediated seed dispersal through fragmented landscapes, uncovering non-random shifts that can have cascading consequences for the composition of regenerating plant communities. Our findings also highlight the importance of forest remnants and frugivore faunas for ecosystem resilience, demonstrating a high potential for passive forest restoration of unmanaged lands in the matrix., European Commission, Award: H2020-MSCA-IF-2014-656572, H2020-MSCA-IF, Agencia Estatal de Investigación, Award: RYC-2017-22095, Ramón y Cajal, Agencia Estatal de Investigación, Award: PID2019-104922GA-I00, Plan Estatal de Investigación, Gobierno del Principado de Asturias, Award: IDI/2018/000151, Proyectos Grupines, Peer reviewed

[Description of methods used for collection/generation of data] Methods are described in Rodríguez and Lopez Darias (2024)., This dataset contains data of PM10 and PM2.5 measured in the Governmental Air quality networks of Spain and Portugal, assessed and reconstructed with the duxt-r method, within the frame of the project AEROEXTREME (PID2021-125669NB-I00) funded by the State Research Agency/Agencia Estatal de Investigación of Spain and the European Regional Development Funds., The data reconstruction of this data set has been performed within the frame of the project AERO-EXTREME (PID2021-125669NB-I00) funded by the State Research Agency/Agencia Estatal de Investigaci—n of Spain and the European Regional Development Funds., File PM10-PM2.5-extreme dust events-Spain and Portugal.zip Contains: dataset-01_PM10_PM25_2001-2022_Spain and Portugal.xlsx dataset-02-reconstruction-dx01-dx02-Feb2020-Canarias.xlsx dataset-03-reconstruction-dx03-Feb2021-Canarias.xlsx dataset-04-reconstruction-dx04-dx05-Jan2022-Canarias.xlsx dataset-05-reconstruction-dx06-March2022-01-Spain.xlsx dataset-06-reconstruction-dx06-March2022-02-Portugal.xlsx, Peer reviewed