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The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

[ES] La base de datos consta de dos archivos en formato .txt con las mallas de anomalías de precipitación (Standardized Precipitation Index) calculadas a 1 y 12 meses sobre el dominio peninsular español, cubriendo el periodo 12/2015_12/2020. Estas han sido calculadas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). Además, se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By). [EN] The database consists of two files in .txt format with the precipitation anomaly grids (Standardized Precipitation Index) calculated at 1 and 12 months over the Spanish peninsular domain, covering the period 2015/12_2020/12. These have been calculated from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). In addition, a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =20 % of the grid area) is included. For each episode we include the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By). [ES] Malla SPI01: texto plano. 5219 filas -descontando el encabezado- y 1261 columnas -descontando las coordenadas X e Y-. Malla SPI12: texto plano. 5219 filas -descontando el encabezado- y 1250 columnas -descontando las coordenadas X e Y-. Archivos descriptivos de los episodios: mapas integrales de duración e intensidad, promedios de SPI’1 y SPI12 y mapas de la propagación espacial. [EN] SPI01 grid: plain text. 5219 rows (excluding the header) and 1261 columns (excluding the X and Y coordinates). SPI12 grid: plain text. 5219 rows (excluding the header) and 1250 columns (excluding the X and Y coordinates). Episode descriptive files: duration and intensity integral maps, SPI01 and SPI12 averages, and spatial propagation maps. Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science. Open Data Commons Attribution (ODC-By 1.0). Peer reviewed

The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI). Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months. Spatial resolution of 0.5º lat/lon. Temporal coverage between January 1901 and December 2011. This is an update of a previous version of SPEIbase v2 (http://hdl.handle.net/10261/48169). What’s new in version 2.2: 1) Data has been extended to the period 1901-2011 (it was 1901-2009 in v 2.0). 2) The FAO grass reference evapotranspiration from the CRU TS3.2 dataset has been used as PET input for the SPEI (http://badc.nerc.ac.uk/browse/badc/cru/data/cru_ts_3.20). Precipitation is also obtained from the same CRU TS3.2 dataset. 3) Unbiased probability weighted moments (ub-pwm) method has been used for fitting the log-Logistic distribution, instead of the sub-optimal plotting-position pwm method used in version 1.0. For more details on the SPEI visit http://sac.csic.es/spei. The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. Use of the newest version is recommended. Older versions are still available to allow replicability.

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.

Continuous phosphorus discharges in bodies of water, generated by human activities, such as agriculture, domestic effluences or wastewater from industrial processes, produce contaminated water and eutrophication. For this reason, efficient and low-cost systems that can remove phosphorus from contaminated water are necessary. In addition, it is important to generate renewable energy such as the energy produced in biomass power plants, taking advantage of the available biomass waste in each place. When producing this renewable energy, the resulting ash is a residue that can be used for phosphorus removal by adsorption processes. Moreover, according to the concept of the circular economy, the ash waste generated in this bio energy process should be reduced as much as possible. One of the advantages of this research being that surplus phosphorus-laden ash can be reused as fertilizer in agricultural fields. Considering this, the efficiency of reed ash (RA) (Phragmites australis) has been analysed in batch experiments, as well as the effect of several parameters on the removal of phosphate, such as contact time, phosphate-ash ratio, ash dose and temperature. Significant results obtained show that RA can be used to improve water quality.

Vegetated coastal ecosystems, in particular mangroves, tidal marshes and seagrasses are highly efficient at sequestering and storing carbon, making them valuable assets for climate change mitigation and adaptation. The state of Queensland, in northeastern Australia, contains almost half of the total area of these blue carbon ecosystems in the country, yet there are few detailed regional or state-wide assessments of their total sedimentary organic carbon (SOC) stocks. We compiled existing SOC data and used boosted regression tree models to evaluate the influence of environmental variables in explaining the variability in SOC stocks, and to produce spatially explicit blue carbon estimates. The final models explained 75 % (for mangroves and tidal marshes) and 65 % (for seagrasses) of the variability in SOC stocks. Total SOC stocks in the state of Queensland were estimated at 569 ± 98 Tg C (173 ± 32 Tg C, 232 ± 50 Tg C, and 164 ± 16 Tg C from mangroves, tidal marshes and seagrasses, respectively). Regional predictions for each of Queensland's eleven Natural Resource Management regions revealed that 60 % of the state's SOC stocks occurred within three regions (Cape York, Torres Strait and Southern Gulf Natural Resource Management regions) due to a combination of high values of SOC stocks and large areas of coastal wetlands. Protected areas in Queensland play an important role in conserving SOC assets in Queensland's coastal wetlands. For example, ~19 Tg C within terrestrial protected areas, ~27 Tg C within marine protected areas and ~ 40 Tg C within areas of matters of State Environmental Significance. Using multi-decadal (1987-2020) mapped distributions of mangroves in Queensland; we found that mangrove area increased by approximately 30,000 ha from 1987 to 2020, which led to temporal fluctuations in mangrove plant and SOC stocks. We estimated that plant stocks decreased from ~45 Tg C in 1987 to ~34.2 Tg C in 2020, while SOC stocks remained relatively constant from ~107.9 Tg C in 1987 to 108.0 Tg C in 2020. Considering the level of current protection, emissions from mangrove deforestation are potentially very low; therefore, representing minor opportunities for mangrove blue carbon projects in the region. Our study provides much needed information on current trends in carbon stocks and their conservation in Queensland's coastal wetlands, while also contributing to guide future management actions, including blue carbon restoration projects.

Advances in the phenology of organisms are often attributed to climate change, but alternatively, may reflect a publication bias towards advances and may be caused by environmental factors unrelated to climate change. Both factors are investigated using the breeding dates of 25 long-term studied populations of Ficedula flycatchers across Europe. Trends in spring temperature varied markedly between study sites, and across populations the advancement of laying date was stronger in areas where the spring temperatures increased more, giving support to the theory that climate change causally affects breeding date advancement.