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This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

Complete dataset (part 1) of daily climate variables (daily precipitation, maximum temperature and minimum temperature) for a historical (1989-2005) and future period (2021-2050), of four climate models (CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR and MPI-M-MPI-ESM-LR) under two Representative Concentration Pathways (RCP 4.5 and 8.5). The climatic variables are provided at 9x9 km resolution for the Iberian Peninsula and at 1x1 km and for the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain). {"references": ["Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., ... & Wulfmeyer, V. (2020). Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community. Regional environmental change, 20(2), 1-20.", "Cornes, R. C., van der Schrier, G., van den Besselaar, E. J., & Jones, P. D. (2018). An ensemble version of the E\u2010OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123(17), 9391-9409."]}

This dataset contains the data used to completed the article under peer review: References: Cabezas, A.; Milanés, Y.;Alba, R.; Delgado, A.M. (2023). The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics. (Article under peer review) Institutions: Spain (Universidad Internacional de La Rioja, Universidad Pablo de Olavide, Hospital Universitario Virgen de las Nieves)

[Experimental design: thermal performance experiments] All experiments were run in climate-controlled incubation facilities of the Institut Mediterrani d’Estudis Avançats (Mallorca, Spain). Following 48 hrs under ambient (collection site) conditions, samples were transferred to individual experimental aquaria, which consisted of a double layered transparent plastic bag filled with 2 L of filtered seawater (60 μm) (following Savva et al. 2018). 16 experimental bags were suspended within 80L temperature-controlled baths. In total, ten baths were used, one for each experimental temperature treatment. Bath temperatures were initially set to the acclimatization temperature (i.e. in situ temperatures) and were subsequently increased or decreased by 1 °C every 24 hours until the desired experimental temperature was achieved. Experimental temperatures were: 15, 18, 21, 24, 26, 28, 30, 32, 34 and 36°C (Table S2). For each species, four replicate aquarium bags were used for each temperature treatment with three individually marked seagrass shoots or three algal fragments placed into each bag. For P. oceanica, each marked plant was a single shoot including leaves, vertical rhizome and roots. For C. nodosa, each marked individual consisted of a 10 cm fragment of horizontal rhizome containing three vertical shoots. Individually marked seaweeds contained the holdfast, and 4-5 fronds of P. pavonica (0.98 ± 0.06 g FW; mean ± SE) or a standardised 5-8 cm fragment with meristematic tip for C. compressa (3.67 ± 0.1 g FW; mean ± SE). Experimental plants were cleaned of conspicuous epiphytes. Once the targeted temperatures were reached in all of the baths, experiments ran for 14 days for the algal species and 21 days for seagrasses to allow for measurable growth in all species at the end of the experiment. Experiments were conducted inside a temperature-controlled chamber at constant humidity and air temperature (15 °C). Bags were arranged in a 4x4 grid within each bath, enabling four species/population treatments to be run simultaneously. Bags were mixed within each bath so that one replicate bag was in each row and column of the grid, to minimise any potential within bath effects of bag position. Replicate bags were suspended with their surface kept open to allow gas exchange and were illuminated with a 14h light:10h dark photoperiod through fluorescent aquarium growth lamps. The water within the bags were mixed with aquaria pumps. The light intensity within each bag was measured via a photometric bulb sensor (LI-COR) and ranged between 180-258 μmol m-2 s-1. Light intensity was constant between experiments and did not significantly differ between experimental treatments (p > 0.05). The temperature in the baths was controlled and recorded with an IKS-AQUASTAR system, which was connected to heaters and thermometers. The seawater within the bags was renewed every 72 hrs and salinity was monitored daily with an YSI multi-parameter meter. Distilled water was added when necessary to ensure salinity levels remained within the range of 36-39 PSU, typical of the study region. Carbon and Nitrogen concentrations in the leaf tissue were measured at the end of the experiment for triplicates of the 24ºC treatment for each species and location (Fig. S2) at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan). [Species description and distribution] The species used in this study are all common species throughout the Mediterranean Sea, although differ in their biological traits, evolutionary histories and thermo-geographic affinities (Fig. S1). P. oceanica is endemic to the Mediterranean Sea with the all other Posidonia species found in temperate Australia (Aires et al. 2011). The distribution of P. oceanica is restricted to the Mediterranean, spanning from Gibraltar in the west to Cyprus in the east and north into the Aegean and Adriatic seas (Telesca et al. 2015) (Fig. S1A). C. nodosa distribution extends across the Mediterranean Sea and eastern Atlantic Ocean, where it is found from south west Portugal, down the African coast to Mauritania and west to Macaronesia (Alberto et al. 2008) (Fig. S1B). Congeneric species of C. nodosa are found in tropical waters of the Red Sea and Indo-Pacific, suggesting origins in the region at least prior to the closure of the Suez Isthmus, approximately 10Mya. Like C. nodosa, Cystoseira compressa has a distribution that extends across the Mediterranean and into the eastern Atlantic, where it is found west to Macaronesia and south to northwest Africa (Fig. S1C). The genus Cystoseira has recently been reclassified to include just four species with all congeneric Cystoseira spp. having warm-temperate distributions from the Mediterranean to the eastern Atlantic (Orellana et al. 2019). The distribution of Padina pavonica is conservatively considered to resemble C. nodosa and C. compressa, spanning throughout the Mediterranean and into the eastern Atlantic. We considered the poleward distribution limit of P. pavonica to be the British Isles 50ºN (Herbert et al. 2016). P. pavonica was previously thought to have a global distribution, but molecular analysis of the genus has found no evidence to support this (Silberfeld et al. 2013). Instead it has been suggested that P. pavonica was potentially misclassified outside of the Mediterranean, due to morphological similarity with congeneric species (Silberfeld et al. 2013). Padina is a monophyletic genus with a worldwide distribution from tropical to cold temperate waters (Silberfeld et al. 2013). Most species have a regional distribution, with few confirmed examples of species spanning beyond a single marine realm (sensu Spalding et al. 2007). [Metabolic rates] Net production (NP), gross primary production (GPP) and respiration (R) were measured for all species from the four sites for five different experimental temperatures containing the in-situ temperature during sampling up to a 6ºC warming (see SM Table S3 for details). Individuals of the different species were moved to methacrylate cylinders containing seawater treated with UV radiation to remove bacteria and phytoplankton, in incubation tanks at the 5 selected temperatures. Cylinders were closed using gas-tight lids that prevent gas exchange with the atmosphere, containing an optical dissolved oxygen sensor (ODOS® IKS), with a measuring range from 0-200 % saturation and accuracy at 25ºC of 1% saturation, and magnetic stirrers inserted to ensure mixing along the height of the core. Triplicates were measured for each species and location, along with controls consisting in cylinders filled with the UV-treated seawater, in order to account for any residual production or respiration derived from microorganisms (changes in oxygen in controls was subtracted from treatments). Oxygen was measured continuously and recorded every 15 minutes for 24 hours. Changes in the dissolved oxygen (DO) were assumed to result from the biological metabolic processes and represent NP. During the night, changes in DO are assumed to be driven by R, as in the absence of light, no photosynthetic production can occur. R was calculated from the rate of change in oxygen at night, from half an hour after lights went off to half an hour before light went on (NP in darkness equalled R). NP was calculated from the rate of change in DO, at 15 min intervals, accumulated over each 24 h period. Assuming that daytime R equals that during the night, GPP was estimated as the sum of NP and R. To derive daily metabolic rates, we accumulated individual estimates of GPP, NP, and R resolved at 15 min intervals over each 24 h period during experiments and reported them in mmol O2 m−3 day−1. A detailed description of calculation of metabolic rates can be found at Vaquer-Sunyer et al. (Vaquer-Sunyer et al. 2015). [Thermal distribution and thermal safety margins] We estimated the realised thermal distribution for the four experimental species by downloading occurrence records from the Global Biodiversity Information Facility (GBIF.org (11/03/2020) GBIF Occurrence Download). Occurrence records from GBIF were screened for outliers and distributions were verified from the primary literature (Alberto et al. 2008, Draisma et al. 2010, Ni-Ni-Win et al. 2010, Silberfeld et al. 2013, Telesca et al. 2015, Orellana et al. 2019) and Enrique Ballesteros (pers. comms) (Fig. S1). Mean, 1st and 99th percentiles of daily SST’s were downloaded for each occurrence site for the period between 1981-2019 using the SST products described above (Table S4). Thermal range position of species at each experimental site were standardised by their global distribution using a Range Index (RI; Sagarin & Gaines 2002). Median SST at the experimental collection sites were standardized relative to the thermal range observed across a species realized distribution, using the equation: RI = 2(SM- DM)/DB where SM = the median temperature at the experimental collection site, Dm = the thermal midpoint of the species global thermal distribution and DB = range of median temperatures (ºC) that a species experiences across its distribution. The RI scales from -1 to 1, whereby ‘-1’ represents the cool, leading edge of a species distribution, ‘0’ represents the thermal midpoint of a species distribution and ‘1’ represents the warm, trailing edge of a species distribution (Sagarin & Gaines 2002). Thermal safety margins for each population were calculated as the difference between empirically derived upper thermal limits for each population and the maximum long term habitat temperatures recorded at collection sites. Each population’s thermal safety margin was plotted against its range position to examine patterns in thermal sensitivity across a species distribution. [Growth measurements and statistical analyses] Net growth rate of seagrass shoots was measured using leaf piercing-technique (Short & Duarte 2001). At the beginning of the experiment seagrass shoots were pierced just below the ligule with a syringe needle and shoot growth rate was estimated as the elongation of leaf tissue in between the ligule and the mark position of all leaves in a shoot at the end of the experiment, divided by the experimental duration. Net growth rate of macroalgae individuals was measured as the difference in wet weight at the end of the experiment from the beginning of the experiment divided by the duration of the experiment. Moisture on macroalgae specimens was carefully removed before weighing them. Patterns of growth in response to temperature were examined for each experimental population using a gaussian function: g = ke[-0.5(TMA-μ)2/σ2], where k = amplitude, μ = mean and σ = standard deviation of the curve. Best fit values for each parameter were determined using a non-linear least squares regression using the ‘nlstools’ package (Baty et al. 2015) in R (Team 2020). 95% CI for each of the parameters were calculated using non-parametric bootstrapping of the mean centred residuals. The relationship between growth metrics and the best-fit model was determined by comparing the sum of squared deviations (SS) of the observed data from the model, to the SS of 104 randomly resampled datasets. Growth metrics were considered to display a significant relationship to the best-fit model if the observed SS was smaller than the 5th percentile of randomised SS. Upper thermal limits were defined as the optimal temperature + 2 standard deviations (95th percentile of curve) or where net growth = 0. Samples that had lost all pigment or structural integrity by the end of the experiment were considered dead and any positive growth was treated as zero. Comparative patterns in thermal performance between populations have fundamental implications for a species thermal sensitivity to warming and extreme events. Despite this, within-species variation in thermal performance is seldom measured. Here we compare thermal performance between-species variation within communities, for two species of seagrass (Posidonia oceanica and Cymodocea nodosa) and two species of seaweed (Padina pavonica and Cystoseira compressa). Experimental populations from four locations spanning approximately 75% of each species global distribution and a 6ºC gradient in summer temperatures were exposed to 10 temperature treatments (15ºC to 36ºC), reflecting median, maximum and future temperatures. Experimental thermal performance displayed the greatest variability between species, with optimal temperatures differing by over 10ºC within the same location. Within-species differences in thermal performance were also important for P. oceanica which displayed large thermal safety margins within cool and warm-edge populations and small safety margins within central populations. Our findings suggest patterns of thermal performance in Mediterranean seagrasses and seaweeds retain deep ‘pre-Mediterranean’ evolutionary legacies, suggesting marked differences in sensitivity to warming within and between benthic marine communities. [Sample collection] Sample collections were conducted at two sites, separated by approximately 1 km, within each location. Collections were conducted at the same depth (1-3 m) at each location and were spaced across the reef or meadow to try and minimise relatedness between shoots or fragments. Upon collection, fragments were placed into a mesh bag and transported back to holding tanks in cool, damp, dark conditions (following Bennett et al. 2021). Fragments were kept in aerated holding tanks in the collection sites at ambient seawater temperature and maintained under a 14:10 light-dark cycle until transport back to Mallorca, where experiments were performed. Prior to transport, P. oceanica shoots were clipped to 25 cm length (from meristem to tip), to standardise initial conditions and remove old tissue for transport. For transport back to Mallorca, fragments were packed in layers within cool-boxes. Cool-packs were wrapped in damp tea towels (rinsed in seawater) and placed between layers of samples. Samples from Catalonia, Crete and Cyprus experienced approximately 12hrs of transit time. On arrival at the destination, samples were returned to holding tanks with aerated seawater and a 14:10 light-dark cycle. [Sea temperature measurements and reconstruction] Sea surface temperature data for each collection site were based on daily SST maps with a spatial resolution of 1/4°, obtained from the National Center for Environmental Information (NCEI, https://www.ncdc.noaa.gov/oisst (Reynolds et al. 2007). These maps have been generated through the optimal interpolation of Advanced Very High Resolution Radiometer (AVHRR) data for the period 1981-2019. Underwater temperature loggers (ONSET Hobo pro v2 Data logger) were deployed at each site and recorded hourly temperatures throughout one year. In order to obtain an extended time series of temperature at each collection site, a calibration procedure was performed comparing logger data with sea surface temperature from the nearest point on SST maps. In particular, SST data were linearly fitted to logger data for the common period. Then, the calibration coefficients were applied to the whole SST time series to obtain corrected-SST data and reconstruct daily habitat temperatures from 1981-2019. [Field collections] Thermal tolerance experiments were conducted on two seagrass species (P. oceanica and Cymodocea nodosa) and two brown seaweed species (Cystoseira compressa and P. pavonica) from four locations spanning 8 degrees in latitude and 30 degrees in longitude across the Mediterranean (Fig. 1, Table S1). These four species were chosen as they are dominant foundation species and cosmopolitan across the Mediterranean Sea. Thermal performance experiments from Catalonia and Mallorca were conducted simultaneously in June 2016 for seaweeds (P. pavonica and C. compressa) and in August 2016 for seagrasses (P. oceanica and C. nodosa). Experiments for all four species were conducted in July 2017 for Crete and in September 2017 for Cyprus. Horizon 2020 Framework Programme, Award: 659246; Juan de la Cierva Formacion, Award: FJCI-2016-30728; Spanish Ministry of Economy, Industry and Competitiveness, Award: MedShift, CGL2015-71809-P; Spanish Ministry of Science, Innovation and Universities, Award: SUMAECO, RTI2018-095441-B-C21

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

Organic waste production has greatly increased following human sprawl and led to the development of landfills in recent decades. This abundant and reliable anthropogenic food source has favoured several species, some of which consequently became overabundant. Landfills present hazards to wildlife, which may suffocate on plastic materials, tangle on cords, and get exposed to pollutants and pathogens. In response to environmental and public health concerns over the maintenance of landfills, the European Commission proposed to close the landfills. Our objective was to determine the impact of the Landfill European Directive on the White Stork, Ciconia ciconia, whose population recovery and growth were linked to landfill exploitation. We implemented species distribution models to project future distribution in the absence of landfills in the Community of Madrid (Spain). Habitat suitability was estimated based on nest occurrence and we included data from land cover types, human population density and two different climate change scenarios (i.e., emissions in low and high shared socioeconomic pathways). Given that protection measures, particularly implemented in protected areas, were associated with population recovery, we also evaluated the overlapping degree between protected areas and projected distribution. Our models predicted a sharp decline in breeding population distribution with landfill closure, reaching values similar to the 1984 breeding census when the species was categorized as threatened. Our results also suggest a decrease in maximum habitat suitability. Climate change also contributed to a reduction in breeding population distribution given model predictions for the extreme emission pathway (ssp5). Measures such as gradual change in landfill management, continuous monitoring of breeding populations, and evaluation of the Stork use of natural feeding areas before and after landfill closure, should be considered.  Direct census searching for nests in the whole Community of Madrid.

Renewable energies will represent an increasing share of the electricity supply, while flue and gasification-derived gases can be a promising CO2 feedstock with a heat load. In this study, microbial electrosynthesis of organic compounds from CO2 at high temperature was proposed as an alternative for valorising energy surplus and decarbonizing the economy. The unremitting fluctuation of renewable energy sources was assessed using two bioreactors at 50 °C, under circumstances of continuous and intermittent power supply (ON-OFF; 8-16 h), simulating an off-grid photovoltaic system. Results highlighted that maximum acetate production rate (43.27 g m-2 d-1) and columbic efficiency (98%) were achieved by working with an intermittent energy supply, while current density was reduced three times. This boosted the production of acetate per unit of electricity provided up to 138 g kWh-1 and reinforced the robustness of the technology by showing resilience to tolerate perturbations and returning to its initial state.

This study aims to characterize and valorize hemp residual biomass by a slow pyrolysis process. The volatile by-products of hemp carbonization were characterized by several methods (TGA, UV-VIS, TLC, Flash Prep-LC, UHPLC, QTOF-MS) to understand the pyrolysis reaction mechanisms and to identify the chemical products produced during the process. The obtained carbon yield was 29%, generating a gaseous stream composed of phenols and furans which was collected in four temperature ranges (F1 at 20–150 °C, F2 at 150–250 °C, F3 at 250–400 °C and F4 at 400–1000 °C). The obtained liquid fractions were separated into subfractions by flash chromatography. The total phenolic content (TPC) varied depending on the fraction but did not correlate with an increase in temperature or with a decrease in pH value. Compounds present in fractions F1, F3 and F4, being mainly phenolic molecules such as guaiacyl or syringyl derivatives issued from the lignin degradation, exhibit antioxidant capacity. The temperature of the pyrolysis process was positively correlated with detectable phenolic content, which can be explained by the decomposition order of the hemp chemical constituents. A detailed understanding of the chemical composition of pyrolysis products of hemp residuals allows for an assessment of their potential valorization routes and the future economic potential of underutilized biomass.

# Meta-data and results for our trend and breakpoint analyses [https://doi.org/10.5061/dryad.d7wm37q6m](https://doi.org/10.5061/dryad.d7wm37q6m) To document flow change, we compiled gauge records from five Mediterranean-climate regions of the world, including California (U.S.), Chile, South Africa, Spain, and Western Australia. For each gauge, we downloaded daily discharge records from public sources (see Open Research Statement and WebTable 1). Next, we limited our analysis to gauges located in Mediterranean-climates zones by retaining the subset of gauges located in Köppen-Geiger climate classes Csa, Csb, Csc (i.e., areas with a dry summer) using maps from Beck et al. 2018. Second, we identified gauges located in minimally disturbed basins. In the US and Australia, we used “reference” gauges identified by the USGS and Bureau of Meteorology, respectively. In South Africa, Chile, and Spain - where reference gauges have not been designated by agencies - we instead used aerial image analysis of upstream watershed conditions to identify basins with no evidence of significant reservoirs or large water infrastructure projects. We note that our determination of “reference-quality” gauges in Spain [excluding Catalonia] is consistent with Messager et al. 2021. Third, we identified gauges with daily data from 1980-2019 (i.e., most recent 40 years in common across the five regions) and no more than one year of missing data. Overall, we identified 158 gauges that met our criteria for inclusion (i.e., Mediterranean-climate, reference-quality, 40 years of data from 1980-2019, and no more than one year of missing data, see WebPanel 1 and WebFigure1). To reduce noise in zero-flow conditions, we defined “zero flows” as flows < 0.1 cfs. Finally, for our analysis of zero-flow trends, we used a liberal definition of “intermittent” and included the subset of streams with ≥ to 1 day/year of zero-flow on average, i.e., ≥ 40 days across the 40 year study, following Messager et al. 2021. Using the population of gauges that met our criteria for inclusion, we conducted trend analyses on daily discharge (for each gauge in our population) and on the annual number of zero-flow days (for the subset of intermittent gauges) across the time series by means of non-parametric Mann-Kendall tests (McLeod 2022). We next explored evidence of flow regime shifts. Specifically, we conducted a breakpoint analysis on the zero-flow days per year using the ‘strucchange’ package in R (Zeileis et al. 2002). We constrained the analysis to test for evidence of a maximum of one breakpoint (indicating a state shift). The meta-data used to run our trend and breakpoint analyses, and the results of those analyses, are presented in this file. **References** Beck HE, Zimmermann NE, McVicar TR, et al. 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5: 180214. McLeod, A.I. (2022). "Kendall: Kendall Rank Correlation and Mann-Kendall trend test". R package version 2.2.1. Available at: [http://cran.r-project.org/package=Kendall](http://cran.r-project.org/package=Kendall). Messager ML, Lehner B, Cockburn C, et al. 2021. Global prevalence of non-perennial rivers and streams. Nature 594: 391–7. Zeileis A, Leisch F, Hornik K, Kleiber C (2002). “strucchange: An R Package for Testing for Structural Change in Linear Regression Models.” Journal of Statistical Software, 7(2), 1–38. doi:10.18637/jss.v007.i02 ## Description of the data and file structure This data file includes columns for meta-data for our analyses ("region", "ID", "latitude", "longitude", "drainage\_area\_km2", "NA\_count"), as well as the results of our trend analyses ("discharge\_tau", "discharge\_p\_value", "zeros\_tau", "zeros\_p\_value") and the results of our breakpoint analyses ("total\_zero\_flow\_days", "BreakpointTime", "MeanZerosBefore", "MeanZerosAfter"). Further detail is provided below. * Region - specifies the Mediterranean-climate region from where the data originated (AU - Australia; CA - California, USA; CH - Chile; SA - South Africa; SP - Spain); * ID - regional ID associated with each gauge record; * latitude - latitude of gauge site; * longitude - longitude of gauge site; * drainage\_area\_km2 - drainage area upstream of each gauge, standardized to units of km2; * discharge\_tau - trend on daily discharge across the time series by means of non-parametric Mann-Kendall tests; * discharge\_p\_value - p-value associated with the trend analysis on daily discharge across the time series by means of non-parametric Mann-Kendall tests; * zeros\_tau - trend on number of annual zero-flow days across the time series by means of non-parametric Mann-Kendall tests; * zeros\_p\_value - p-value associated with the trend analysis on the annual number of zero-flow days across the time series by means of non-parametric Mann-Kendall tests; * NA\_count - a check that we included only gauge records with less than one year of missing data (i.e., for all gauge records included in our analyses, the count of missing data or "NAs" < 365); * total\_zero\_flow\_days - the total number of zero-flow days across the time series, used to identify the subset of "intermittent" and "perennial" gauges (we used a liberal definition of “intermittent” and included the subset of streams with ≥ to 1 day/year of zero-flow on average, i.e., ≥ 40 days across the 40 year study, following Messager et al. 2021); * BreakpointTime - we conducted a breakpoint analysis on the zero-flow days per year and constrained the analysis to test for evidence of a maximum of one breakpoint (indicating a state shift). For the subset of gauges showing evidence of a state shift, we report the year (ranging from the 1st to the 40th year across the time series) associated with the shift as the "BreakpointTime"; * MeanZerosBefore - For the subset of gauges showing evidence of a state shift, we further report the mean number of zero-flow days before the state shift; * MeanZerosAfter - For the subset of gauges showing evidence of a state shift, we further report the mean number of zero-flow days after the state shift. ## Sharing/Access information The gauge data sets utilized for this research were retrieved from the following sources: * Australia - Australian Government, Bureau of Meteorology, Water data online ([http://www.bom.gov.au/waterdata](http://www.bom.gov.au/waterdata)); * California, USA - USGS National Water Information System, USGS Water Data for California ([https://waterdata.usgs.gov/ca/nwis/](https://waterdata.usgs.gov/ca/nwis/)); * Chile - CAMELS-CL explorer (CR)2 ([https://camels.cr2.cl/](https://urldefense.com/v3/__https:/camels.cr2.cl/__;!!D9dNQwwGXtA!VUyljJtmgJsBqSnUMlOHRpds_SLFQHcPi6yYQCph6JPABduySWBpXgy_GBdu1mOihz82D--9A4bnOUyP_Jq79JQ3$)) from Alvarez-Garreton et al. 2018; * South Africa - Republic of South Africa, Department Water and Sanitation, Hydrological Services - Surface Water ([https://www.dws.gov.za/Hydrology/Verified/hymain.aspx](https://urldefense.com/v3/__https:/www.dws.gov.za/Hydrology/Verified/hymain.aspx__;!!D9dNQwwGXtA!VXV4ikJ5GqtpAPzYvj7lfVPS4xbEFbmw4ZNdI8Wtz5pCrLk7OYMIVdetRnWSyctJIh_1bydu4pggv63bc_fSHasLgQ$)); * Spain - Centro de Estudios Hidrográficos (CEDEX) ([https://ceh.cedex.es/anuarioaforos/default.asp](https://ceh.cedex.es/anuarioaforos/default.asp)) and Agència Catalana de l’Aigua: [https://aplicacions.aca.gencat.cat/sdim21/seleccioXarxes.do](https://urldefense.com/v3/__https:/aplicacions.aca.gencat.cat/sdim21/seleccioXarxes.do__;!!D9dNQwwGXtA!VUyljJtmgJsBqSnUMlOHRpds_SLFQHcPi6yYQCph6JPABduySWBpXgy_GBdu1mOihz82D--9A4bnOUyP_G5pTfaN$). ## To document flow change, we compiled gauge records from five Mediterranean-climate regions of the world, including California (U.S.), Chile, South Africa, Spain, and Western Australia. For each gauge, we downloaded daily discharge records from public sources. Next, we limited our analysis to gauges located in Mediterranean-climates zones by retaining the subset of gauges located in Köppen-Geiger climate classes Csa, Csb, Csc (i.e., areas with a dry summer) using maps from Beck et al. 2018. Second, we identified gauges located in minimally disturbed basins. In the US and Australia, we used “reference” gauges identified by the USGS and Bureau of Meteorology, respectively. In South Africa, Chile, and Spain - where reference gauges have not been designated by agencies – we instead used aerial image analysis of upstream watershed conditions to identify basins with no evidence of significant reservoirs or large water infrastructure projects. We note that our determination of “reference-quality” gauges in Spain [excluding Catalonia] is consistent with Messager et al. 2021. Third, we identified gauges with daily data from 1980-2019 (i.e., most recent 40 years in common across the five regions) and no more than one year of missing data. Overall, we identified 158 gauges that met our criteria for inclusion (i.e., Mediterranean-climate, reference-quality, 40 years of data from 1980-2019, and no more than one year of missing data, WebPanel 1, WebFigure1). To reduce noise in zero-flow conditions, we defined “zero flows” as flows < 0.1 cfs. Finally, for our analysis of zero-flow trends, we used a liberal definition of “intermittent” and included the subset of streams with ≥ to 1 day/year of zero-flow on average, i.e., ≥ 40 days across the 40 year study, following Messager et al. 2021. Using the population of gauges that met our criteria for inclusion, we conducted trend analyses on daily discharge (for each gauge in our population) and on the annual number of zero-flow days (for the subset of intermittent gauges) across the time series by means of non-parametric Mann-Kendall tests. We next explored evidence of flow regime shifts. Specifically, we conducted a breakpoint analysis on the zero-flow days per year using the ‘strucchange’ package in R. We constrained the analysis to test for evidence of a maximum of one breakpoint (indicating a state shift).  Stream drying is happening globally, with significant ecological and social consequences. Most examples of stream drying come from systems influenced by dam operations or those with highly exploited aquifers. Stream drying is also thought to be happening due to climate change, but examples are surprisingly limited. We explored flow trends from the five Mediterranean-climate regions with a focus on unregulated streams with long-term gauge records. We found consistent evidence of decreasing discharge trends, increasing zero-flow days, and steeper downward discharge trends in smaller basins. Beyond directional trends, many systems recently shifted flow state, including some streams that shifted from perennial to intermittent flow states. Our analyses provide evidence of stream drying consistent with climate change, but also highlight knowledge gaps and challenges in empirically and statistically documenting flow regime shifts. We discuss the myriad consequences of losing flow and propose strategies for improving detection and adapting to flow change.

In the context of our project, we organised a webinar at which almost 200 participants assisted. It was aimed at everyone who cares about a greener and more sustainable future. The development of sustainable and resource-saving processes is a major focus of R&D&I work, also supported heavily by the European Commission as part of the Green Deal and the sustainability efforts. In this context, biotechnology is already acting as a facilitator to achieve a circular economy and a bioeconomy. We aim to achieve these goals with the identification, optimisation, production and application of innovative enzymes to support the transformation of various industrial sectors and their consumer products. In this webinar, we wanted to present the competences and topics we acquire or work on in FuturEnzyme to an interested international audience. With this CLIB Forum event, we want to emphasise and promote the need for collaboration between researchers, entrepreneurs, and manufacturers for a greener and more sustainable future. Furthermore, our webinar was also of interest for policy makers, funding bodies, investors and consumers. The FuturEnzyme project partners CSIC, Barcelona Supercomputing Center and the University of Hamburg presented their activities in the project and beyond to a wide range audience.