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Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain. 

Anthropogenic structures (e.g. weirs and dams) fragment river networks and restrict the movement of migratory fish. Poor understanding of behavioural response to hydrodynamic cues at structures currently limits the development of effective barrier mitigation measures. This study aimed to assess the effect of flow constriction and associated flow patterns on eel behaviour during downstream migration. In a field experiment, we tracked the movements of 40 tagged adult European eels (Anguilla anguilla) through the forebay of a redundant hydropower intake under two manipulated hydrodynamic treatments. Interrogation of fish trajectories in relation to measured and modelled water velocities provided new insights into behaviour, fundamental for developing passage technologies for this endangered species. Eels rarely followed direct routes through the site. Initially, fish aligned with streamlines near the channel banks and approached the intake semi-passively. A switch to more energetically costly avoidance behaviours occurred on encountering constricted flow, prior to physical contact with structures. Under high water velocity gradients, fish then tended to escape rapidly back upstream, whereas exploratory ‘search’ behaviour was common when acceleration was low. This study highlights the importance of hydrodynamics in informing eel behaviour. This offers potential to develop behavioural guidance, improve fish passage solutions and enhance traditional physical screening. Fish_detections_UL_CHFish positions derived from acoustic telemetry contained within excel file with 5 columns. 'Record' denotes tag detection numbered consecutively in sequence; 'tag_number' denotes the fish identification number; ‘PosX’ denotes fish x coordinate in UTM; ‘PosY’ denotes fish y coordinate in UTM, ‘Treatment’ denotes experimental treatment

We discontinued this version of the dataset.

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

Velocity-based macrorefugia for boreal passerine birds Citation for dataset -------------------- Stralberg, D. Velocity-based macrorefugia for boreal passerine birds. Boreal Avian Modelling Project. Edmonton, Alberta, Canada. DOI: 10.5281/zenodo.1299880 https://doi.org/10.5281/zenodo.1299880 Data layers ----------------- Refugia layers represent mid-century (2041-2070) and end-of-century (2071-2100) conditions for the SRES A2 emissions scenario at 4-km resolution ----------------- Combined index for 53 species (clipped to Brandt's boreal region): _refbrandt53_YYYYZZZZ Species-specific indices: XXXX_refYYYY where: YYYY = Time period (2050s or 2080s) ZZZZ = weighted or unweighted XXXX = Songbird Species Code (see Birdlookup.csv) Percentile values of refugia indices for mapping purposes 0.01 0.1 0.25 0.5 0.75 0.9 0.99 "2050s, weighted " 0.032 0.243 0.317 0.399 0.484 0.589 0.779 "2080s, weighted" 0.002 0.09 0.137 0.2 0.281 0.386 0.675 "2050s, unweighted" 0.006 0.108 0.159 0.218 0.292 0.358 0.421 "2080s, unweighted" 0.001 0.055 0.083 0.123 0.185 0.241 0.297 Projection information ------------------- """+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs""" ------------------- Projection LAMBERT Spheroid GRS80 Units METERS Zunits NO Xshift 0.0 Yshift 0.0 Parameters 49 0 0.0 /* 1st standard parallel 77 0 0.0 /* 2nd standard parallel -95 0 0.0 /* central meridian 0 0 0.0 /* latitude of projection's origin 0.0 /* false easting (meters) 0.0 /* false northing (meters)

This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. The data are in arcASCII format. All data are in units of standard Euclidean distance and multiplied by 1000. This is the original data. To scale the data similar to Tabor et al. (2018), remove outliers above the 99th percentile distribution before rescaling from 0-1. Unprojected number of columns 2160 number of rows 857 Lower Left X Center -179.917 Lower Left Y Center -59.084 Cell size 0.166667 decimal degrees (10 minutes or ~17 km) {"references": ["Tabor, K. et al. (2018). Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation: https://doi.org/10.3390/land7030090", "Tabor and Williams (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. https://doi.org/10.1890/09-0173.1", "Williams, J.W. et al. (2007). Projected distributions of novel and disappearing climates by 20100 AD. https://doi.org/10.1073/pnas.0606292104"]} Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.