Resultados de investigación

Feeding rates, fecal production rates, egg production rates and gross-growth efficiencies of the copepod Paracartia grani offered a variety of phytoplankton, protozooplankton and mixoplankton in monodiet. See Excel file for details, This research was supported by EC MSCA-ITN 2019 funding to the project MixITiN (Grant Number 766327), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

The datasets contain measures of length and width of the egg of three species of Seabirds., Measures of egg volume of three seabirds: the Scopoli's shearwater Calonectis diomedea, the Sandwich tern Thalasseus sandvicensis and the Audouin’s gull Ichthyaetus audouinii in three colonies of the Western Mediterranean., No

This data set contains the underlying data of two scientific publications: Lobo Prieto, Ana; Tena, Noelia; Aparicio-Ruiz, Ramón; Morales, María T.; García-González, Diego L.; 2020; Tracking sensory characteristics of virgin olive oils during storage: Interpretation of their changes from a multiparametric perspective. Molecules 25, 1686. Food Control 123, 107823. https://doi.org/10.3390/molecules25071686. Lobo-Prieto, Ana; Tena, Noelia; Aparicio-Ruiz, Ramón; García-González, Diego L.; Sikorska, Ewa; 2020. Monitoring virgin olive oil shelf-life by fluorescence spectroscopy and sensory characteristics: A multidimensional study carried out under simulated market conditions. Foods 9, 1846. https://doi.org/10.3390/foods9121846. Virgin olive oil is inevitably subject to an oxidation process during storage that can affect its quality due to off-flavors that develop before the oil surpasses its ‘best before’ date. Due to the complexity of the oxidation process at moderate conditions, where many parameters are involved, a multiparametric study is necessary to understand globally the physical-chemical changes and sensory quality degradation in a real storage experiment. In this context, a storage experiment of 27 months was performed for four monovarietal virgin olive oils, bottled in transparent 500-mL PET bottles and subjected to conditions close to a supermarket scenario (1000 lx with 12 hours of light/dark cycles and 25 °C). Physical-chemical quality parameters, phenolic compounds, α-tocopherol concentration and the degradation products of chlorophyll a were determined every month. Simultaneously, an accredited sensory panel assessed their sensory characteristics. The different physical-chemical quality parameters studied (peroxide value, free acidity, K232 and K270) were determined following the International Olive Council (COI) standard methods. The concentration of total phenols was determined by solid phase extraction and HPLC analysis, following the method described by Aparicio-Ruiz et al. (2016), and the α-tocopherol concentration was determined using the method ISO 9936:2016. Finally, the degradation compounds of chlorophyll a (pheophytin a and pyropheophytin a) were measured using the method specified by ISO 29841:2012. The dataset presents the results of all the parameters mentioned above for the four monovarietal virgin olive oils subjected to storage at moderate conditions during 27 months., Project Info: “Nuevas estrategias de verificación del tiempo de vida útil y la fecha de consumo preferente del aceite de oliva virgen mediante indicadores espectroscópicos” and “Materiales de referencia de notas sensoriales positivas de aceites de oliva vírgenes extra basado en marcadores volatiles y estudios de interacción aroma-aroma“, funded by State Research Agency. Proyectos AGL2015-69320-R and RTI2018-101546-B-C21/22 financiado por MCIN/ AEI /10.13039/501100011033/ FEDER “Una manera de hacer Europa”., The dataset consists of: a numerical quantitative file saved both in .xlsx and .ods formats (“VOO_storage_xlsx.xlsx” - “VOO_storage _ods.ods”. A README file “VOO_storage_README_rtf.rtf”, Peer reviewed

The metadata associated with the global field survey are publicly available in Figshare https://doi.org/10.6084/m9.figshare.11484747, Dataset from "Delgado-Baquerizo et al. The proportion of soil-borne pathogens increases with warming at the global scale" http://hdl.handle.net/10045/107291, Peer reviewed

[Description of methods used for collection/generation of data] We built a genus-level phylogeny using the workflow proposed by Roquet et al. (2013). We downloaded from Genbank three conserved chloroplastic regions (rbcL, matK and ndhF) plus the ITS region for a subset of families, which we aligned separately by taxonomic clustering. [Methods for processing the data] We aligned all coding sequence clusters with MACSE (Ranwez et al. 2011) and non-coding ones with MAFFT (Katoh and Standlye 2013), and trimmed all alignments with TrimAl (Capella-Gutiérrez et al. 2009). We concatenated all alignments to obtain a supermatrix. We then conducted maximum-likelihood (ML) phylogenetic inference analyses with RAxML (Stamatakis 2014), applying the most appropriate partitioning scheme and substitution model obtained with PartitionFinder (Lanfear et al. 2012) and a supertree constraint at the family-level obtained with the online software Phylomatic v.3 (tree R20120829). Specifically, we performed 100 independent tree searches and selected the best ML tree (the one with the highest probability). [Instrument- or software-specific information needed to interpret/reproduce the data] Any software for manipulation or analysis of phylogenies such as R packages ape or picante. [Standards and calibration information, if appropriate] The best ML tree was dated applying the penalized likelihood method in treePL (Smith and O'Meara,2012) and the following node calibrations: we fixed the node corresponding to the ancestor of eudicots at 125 Ma based on the earliest eudicot fossil (Hughes and McDougall 1990), and applied minimum age constraints to 15 nodes based on fossil information extracted from Smith and Beaulieu (2010) and Bell et al. (2010). 5. Environmental/experimental conditions:, Agencia Estatal de Investigación, Project VULVIMON (Reference: CGL2017-90040-R)., Peer reviewed

Volcanic eruptions produce health changes in animals that may be associated with emitted gases and deposited ashes. We evaluated whether the Puyehue–Cordón Caulle volcanic eruption in 2011 produced health changes in the threatened Andean condor (Vultur gryphus) living in the area most affected by the eruption, north-western Patagonia. We studied clinical and biochemical parameters of condors examined before and after the eruption. We also examined concentrations of different metals and metalloids in the blood of individuals sampled after the eruption. The most common clinical abnormality associated with the eruptive process was irritating pharyngitis. In condors sampled after the eruption, blood concentrations of albumin, calcium, carotenoids and total proteins decreased to levels under the reference values reported for this species. We found different chemical elements in the blood of these condors after the eruption, such as arsenic and cadmium, with the potential to produce health impacts. Thus, the health of Andean condors was affected in different ways by the eruption; remaining in the affected area appears to have been costly. However, in comparison to other animal species, the health impacts were not as strong and were mainly related to food shortages due to the decrease in availability of livestock carcasses linked to the eruption. This suggests that condors dealt relatively well with this massive event. Future research is needed to evaluate if the health changes we found reduce the survival of this species, and if the cost of inhabiting volcanic areas has any ecological or evolutionary influence on the condor’s life history., Peer reviewed

[Methods] Dr Carlos Palacin-Lizarbe made the datasets. For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019)., [Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95 This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). See this publication for further details on the methods used. Dataset 1: Sediment_denitrification_rates_DRYAD.txt Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site. Dataset 2: table_background_DRYAD_def.txt Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Variables contained in the datasets: id_rate: identification code for each denitrification rate measurement is useful as a link between datasets. id_core: identification code for each sediment core is useful as a link between datasets. yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets. denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment. actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition. nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose. nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose. nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose. high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose. nitrate_treatment: treatment of nitrate addition in the denitrification measurements. Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added). temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement. nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core. glucose_added (mg * L-1): added when nitrate was also added. site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV). latitude (N). longitude (E). altitude (m a.s.l.). lake_area (ha). catchment_area (ha). renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time. habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments. temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved. nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats. sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features. organic_matter (%): sediment organic matter content determined by Lost on Ignition. nitrogen (%): sediment nitrogen content. d15N (parts per thousand): sediment delta15N. carbon (%): sediment carbon content. d13C (parts per thousand): sediment delta13C. carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements. dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio. sediment_density (g * cm-3). sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK). DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm). X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm). nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm). nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm). nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm). nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm)., During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this., Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P., Peer reviewed

[Methods] This submission contains all the R code used for numerical predictions and simulations presented in the paper. Selected simulation results are also included, to allow the reader quick access to results without having to run some of these (computationally intensive) scripts. [Usage Notes] The R code and simulation results are organized according to the figures presented in the data, to help easily find the relevant code., Multipartite viruses have two or more genome segments, and package different segments into different particle types. Although multipartition is thought to have a cost for virus transmission, its benefits are not clear. Recent experimental work has shown that the equilibrium frequency of viral genome segments, the setpoint genome formula (SGF), can be unbalanced and host-species dependent. These observations have reinvigorated the hypothesis that changes in genome-segment frequencies can lead to changes in virus-gene expression that might be adaptive. Here we explore this hypothesis by developing models of bipartite virus infection, leading to a threefold contribution. First, we show that the SGF depends on the cellular multiplicity of infection (MOI), when the requirements for infection clash with optimizing the SGF for virus-particle yield per cell. Second, we find that convergence on the SGF is very rapid, often occurring within a few cellular rounds of infection. Low and intermediate MOIs lead to faster convergence on the SGF. For low MOIs this effect occurs because of the requirements for infection, whereas for intermediate MOIs this effect is also due to the high levels of variation generated in the genome formula. Third, we explored the conditions under which a bipartite virus could outcompete a monopartite one. As the heterogeneity between environments and specificity of gene-expression requirements for each environment increased, the bipartite virus was more likely to outcompete the monopartite virus. Under some conditions changes in the genome formula helped to exclude the monopartite competitor, highlighting the versatility of the genome formula. Our results show the inextricable relationship between MOI and the SGF, and suggest that under some conditions the cost of multipartition can be outweighed by its benefits for the rapid tuning of viral gene expression., Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Award: 016.Vidi.171.061. Agencia Estatal de Investigación, Award: FEDER grant BFU2015-65037-P. Generalitat Valenciana, Award: PROMETEU/2019/012., Peer reviewed

In the marine realm, biomonitoring using eDNA of benthic communities requires destructive direct sampling or the setting-up of settlement structures. Comparatively much less effort is required to sample the water column, which can be accessed remotely. In this study we assess the feasibility of obtaining information from the eukaryotic benthic communities by sampling the adjacent water layer. We studied two different rocky-substrate benthic communities with a technique based on quadrat sampling. We also took replicate water samples at distances from a few centimetres to 20 m from the benthic habitat. Using as marker a fragment of the Cytochrome c oxidase subunit I gene with universal primers, we obtained a total of 3,543 molecular operational taxonomic units (MOTUs) from the samples. The structure obtained in the two environments was markedly different, with Metazoa, Archaeplastida Rhodophyta and Stramenopiles being the most diverse group in benthic samples, and HacrobiaAlveolata, Metazoa and Alveolata Rhizaria in the water. Only 265 MOTUs (7.5%) were shared between benthos and water samples, and of these 180 MOTUs (5.1%) were identified as benthic MOTUs that left their DNA in the water. Most of them were found immediately adjacent to the benthos, and their number decreased and The distribution of these benthic shared MOTUs showed a decrease both in number of MOTUs and in number of reads as we moved apart from the benthic habitat. It was concluded that water eDNA, even in the close vicinity of the benthos, was a poor proxy for the analysis of benthic structure, and that direct sampling methods are required for monitoring these complex benthic communities via metabarcoding., Peer reviewed

[Methods] The dataset was collected by four different teams who took part in the project. It consisted on four case studies of forest regrowth (including expansion and densification) after rural abandonment with contrasting ecological and societal contexts. The study took place in Spain and France. Two landscapes are located in rural areas undergoing human exodus and forest expansion and densification; the other two, in peri-urban areas with intense land use and forest densification but negligible expansion. For each forest plot, we estimated variables related to ten out of the 18 main Nature's contributions to people (NCP) defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Regulating and material NCP were addressed using variables measured in the field as proxies. Non-material NCP were studied through stakeholder interviews. Thus, this dataset contains data on NCP associated with forest regrowth of Juniperus thurifera L., Fagus sylvatica L., Quercus ilex L., and Quercus robur L stands. It includes a total of 65 study plots and 2,837 individual trees. The considered NCP included four regulating, three material, and three non-material NCP as well as one NCP common to all categories. The four regulating NCP were habitat creation and maintenance; pollination and dispersal of seeds and other propagules; regulation of climate through biological carbon (C) sequestration and storage; and regulation of detrimental organisms and biological processes. The two material NCP were energy; and medicinal, biochemical and genetic resources. The three non-material NCP were learning and inspiration; physical and psychological experience; and supporting identities. The NCP common to all categories was the maintenance of options, reflected in maintaining biodiversity (estimated in this case with the Shannon diversity index). The dataset contains information at both individual and plot level. Habitat creation and maintenance (NCP1) was calculated by computing the spatial connectivity of the plots in Q. ilex and Q. robur stands. It was inferred by calculating the percentage cover of broadleaved forest in a circular buffer (radius = 500 m) around each plot. Pollination and the dispersal of seeds and other propagules (NCP2) was estimated by counting all seedlings and saplings in each plot and divided them by the plot area to obtain the density of saplings per hectare. Climate regulation in terms of biological carbon (C) storage and sequestration (NCP4) was estimated by the overall C stock contained in the trees of the study plots. We calculated the total biomass per tree using species-specific allometric equations that combine the dbh and the height of the sampled trees. We also calculated the C stock per tree multiplying the obtained biomass by the percentage of C in each species. The regulation of detrimental organisms and biological processes (NCP10) was assessed using the percentage of invertebrate herbivory. For all except the J. thurifera case study, we determined herbivore damages by visually estimating the percentage of leaf area removed by invertebrates. Energy provision (NCP11) was understood as the production of biomass-based fuels such as fuelwood. We estimated biomass input from thick and medium branches (i.e. the tree parts normally employed as fuelwood) for each tree within plots. Biomass input from thick and medium branches was calculated from allometric equations specific for each species. The provision of medicinal, biochemical and genetic resources (NCP14) includes the production of plant genes and genetic information. In each plot we quantified gene diversity corrected for sample size as proxy for NCP14. The maintenance of options (NCP18) includes the benefits associated with species diversity. We scored woody species richness and abundance and computed the Shannon diversity index as proxy. Additionally, interviews regarding social perceptions related to learning and inspiration (NCP15), physical and psychological experiences (NCP16) and supporting identities (NCP17) were conducted at the case study level. Please notice that the social perception dataset is not uploaded to ensure data privacy policy. More information and detailed Methodology can be found in Martín-Forés et al. (2020) People and Nature, in both the main text and the Supplementary Material S1. [Usage Notes] Some values are missing because the methodology was adjusted according to each case study. For more information please read the detailed information provided by Martín-Forés et al. (2020) People and Nature, in both the main text and the Supplementary Material S1., [Context] European forests are expanding and becoming denser following the widespread abandonment of farmland and rural areas. Yet, little is known about the goods and services that spontaneous forest regrowth provide to people., [Aims] We assessed the changes in nature’s contributions to people (NCP) from spontaneous forest regrowth, i.e. forest expansion and densification, in South-West Europe., [Methods We investigated 65 forest plots in four different landscapes with contrasting ecological and societal contexts. Two landscapes are located in rural areas undergoing human exodus and forest expansion and densification; the other two, in peri-urban areas with intense land use and forest densification but negligible expansion. For each forest plot, we estimated variables related to ten out of the 18 main NCP defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Regulating and material NCP were addressed using variables measured in the field as proxies. Non-material NCP were studied through stakeholder interviews., [Results] Our results show across the cases that forest expansion and densification is generally associated with greater climate regulation and energy provision. Changes in other NCP, especially in non-material ones, were strongly context-dependent. The social perception of spontaneous forest regrowth was primarily negative in rural areas and more positive in peri-urban landscapes., [Conclusion] Passive restoration through spontaneous forest expansion and densification can enhance regulating and material NCP, especially when adaptive management is applied. To optimise NCP and to increase the societal awareness of and interest in spontaneous forest regrowth, the effects of this process should be analysed in close coordination with local stakeholders to unveil and quantify the many and complex trade-offs involved in rural or peri-urban social perceptions., BiodivERsA, Award: BiodivERsA3-2015-58. Agencia Estatal de Investigación, Award: PCIN-2016-055. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2017-83170-R. Deutsche Forschungsgemeinschaft. BiodivERsA, Award: BiodivERsA3-2015-58., Peer reviewed