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Este documento presenta los datos de biomasa en carbono (mgC m-2) de taxones planctónicos y perifíticos de un experimento a escala de mesocosmos. La comunidad planctónica-perifítica fue sometida a tres escenarios de cambio global, con la temperatura y la radiación ultravioleta como factores experimentales testados independientemente. Cada elemento de la comunidad fue identificado a la máxima resolución taxonómica posible. Las abreviaciones y explicaciones sobre los escenarios experimentales, las replicas de cada escenario y los detalles de los compartimentos considerados en cada mesocosmos se muestran en la primera página de este documento., This document presents the data about carbon biomass (mgC m-2) of planktonic and periphytic taxa identified in a mesocosm experiment. The planktonic-periphytic community was subjected to three global change-related scenarios, with temperature and ultraviolet radiation as independently tested factors. Each element of this community was identified at the highest possible taxonomic resolution. Abbreviations about the experimental scenarios, the replicates and the details of the considered compartments in each mesocosm are shown on the first page of the document.

Two databases: Research on happiness and affect during Covid-19 confinement

Esta base de datos contiene los valores de diversos índices topológicos calculados a partir de los nodos funcionales de las comunidades acuáticas plánctónicas-bentónicas de un experimento a escala de mesocosmos. Mediante un diseño experimental que constaba de tres escenarios ambientales con cuatro réplicas cada uno (con la temperatura y la radiación ultravioleta como factores experimentales), se agruparon los organismos presentes en cada réplica (mesocosmos) en nodos funcionales. Con estos nodos se construyeron redes ecológicas tróficas (solo considerando relaciones tróficas entre ellos) y redes multi-interacción (considerando relaciones tróficas y no-tróficas). A cada uno de los nodos en cada versión de la red ecológica se le calcularon diversos índices (topological importance index, toplogical overlap index, closeness centrality y betweenness centrality) que muestran su importancia topológica en la red. Las abreviaciones y explicaciones sobre los escenarios experimentales, las replicas de cada escenario y los detalles sobre los índices y los nodos de las redes están detallados en la primera hoja de esta base de datos., This database contains the values of several topological indices calculated from the functional nodes of the planktonic-benthic aquatic communities from a mesocosm-scale experiment. Using an experimental design consisting of three environmental scenarios with four replicates each (with temperature and ultraviolet radiation as experimental factors), the organisms present in each replicate (mesocosm) were grouped into functional nodes. With these nodes, ecological trophic networks (only considering trophic relationships between them) and multi-interaction networks (considering trophic and non-trophic relations) were constructed. Different indices (topological importance index, topological overlap index, closeness centrality, and betweenness centrality) were calculated for each of the nodes in each version of the ecological network, showing their topological importance in the network. The abbreviations and explanations about the experimental scenarios, the replicates of each scenario, and the details regarding the indices and the nodes of the networks are detailed in the first sheet of this database.

Los datos aqui presentados comprenden la ocurrencia y abundancia relativa de las diatomeas bentónicas identificadas en sistemas acuáticos de la Serranía de Cuenca (centro de España) durante el verano de 2017. Los taxones se ordenan en la matriz alfabéticamente y se les asigna un código. Las muestras se caracterizan tambien por un código y por información sobre su cuenca de origen (rio Tajo o Júcar), sustrato (mineral o planta) y hábitat (ambientes lénticos o lóticos). Toda esta información contenida en varias página de un documento excel., The data presented in this document comprised the occurrence and relative abundance of benthic diatom taxa identified in Serranía de Cuenca (central Spain) waters during summer of 2017. Taxa are ordered alphabetically in columns and named with a code (see the "Taxa code" sheet) The sample code and information on catchments (Tajo or Júcar rivers), substrate (mineral or plant) and habitat (stream or stagnant water) are reported in the "Sample code" sheet

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

[Methods] Bicyclopyrone sorption was determined on each soil in triplicate using the batch equilibration method with radiolabeled compounds. The 14C spiking solution for BCP was prepared by diluting 14C-bicyclopyrone [pyridinyl-3-14C; specific activity = 95.3 µCi mg-1; 8,880 Bq mmol-1; 99.5% purity] with unlabeled BCP (Syngenta Crop Protection, LLC) to result in a stock solution of 10 mg L−1 with an overall activity of 1,090 Bq mL-1 (65,450 DPM mL-1). A 0.5 mL aliquot of this spiking solution was added to 9.5 mL of 0.01 M CaCl2 to achieve a 10 mL solution volume. This solution (0.5 mg L−1 BCP) was added to 1 g of soil in a 20 mL glass scintillation vial for a series of 25 different USDA soil series. Samples were then placed horizontally on a reciprocal shaker and allowed to equilibrate for 24 h (180 rev min-1) in the dark. Following centrifugation (20 min, 1500 × g), 2 mL of supernatant was removed and filtered (0.45 μm). Then a 1 mL aliquot of the filtered solution was mixed with 5 mL of scintillation cocktail [EcoLite(+)™; MP Biomedicals, LLC, Solon, OH] and analyzed for 14C by liquid scintillation counting (LSC) (HITACHI AccuFLEX LSC-8000, GMI Ramsey, MN, 10-minute counting window). No statistically significant sorption of the BCP to scintillation vials, syringes or syringe filters was observed (98-100% recovery, data not shown). The sorbed concentration of BCP on the soil was estimated by the following: Cs=Ci-CemV , where Ci is the initial concentration (mg L-1), Ce is the equilibrium concentration (mg L-1), V is the total volume of the liquid phase (L), and m is the mass of the soil (g). The equilibrium concentration was determined by the LSC of the liquid phase using the following equation: Ce=LSCe-Blank * CiLSCi , where LSCe is the disintegrations per minute of the sample following equilibration with the soil, Blank is the disintegrations per minute of the scintillation cocktail and vial alone, Ci is the liquid phase concentration of the initial standard (mg L-1), and LSCi are the disintegration of the corresponding BCP standards (without soil added). The partitioning coefficient (KD) is estimated by the following: KD= CSCe . Sorption Isotherms – Multiple Concentrations Sorption isotherms were generated for 7 selected soils which were selected based on the initial screening above for a range of observed KD values. The isotherm was determined utilizing initial BCP solution concentrations of 0.1, 0.5, 1, 2.5, 5, 10, and 20 mg L−1. Individual samples were run in duplicate using methods analogous to the single concentration batch KD determination described earlier. Equilibrium liquid (Ce) and solid (Cs) concentrations were then analyzed by fitting to different linear forms of four sorption models: Langmuir, Freundlich, Temkin, and the Dubinin-Radushkevich models (Horsfall, Spiff, & Abia, 2004; Hunt & He, 2015). [Usage Notes] Excel file with separate worksheets for the batch-isotherm (KD) and the multi-point isotherm datasets., Bicyclopyrone is a herbicide that is targeted for the control of herbicide-resistant weeds. However, there is a lack of extensive data on its sorption and factors that control its sorption in the soil system. In this study, we evaluated a series of 25 different soils, with a variety of soil properties to assess if an empirical relationship could be developed to predict the sorption coefficient for bicyclopyrone. Overall, there were no statistically significant relationships observed with organic carbon, cation exchange capacity, or clay content. There solely was a moderate negative correlation with soil pH (R=-0.65). Additionally, Freundlich isotherm analysis suggests that the KD could be adequate to characterize the sorption behavior for the range of soils evaluated here., Peer reviewed