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  • This data resource consists of two files: (a) 1x1 km resolution Average Accumulated Exceedance (AAE) data summarising the exceedances of acidity critical loads for eight habitats; (b) 1x1 km resolution AAE data summarising the exceedances of nutrient nitrogen critical loads for thirteen habitats. The data provide information on the amount of excess acid or nitrogen deposition above the critical load values set to protect acid- and nitrogen-sensitive habitats in the UK. The AAE has been calculated using UK 5x5 km Concentration Based Estimated Deposition (CBED) data for 2017-19 (https://doi.org/10.5285/1efa692d-76ca-406e-8736-837a457e16ee). The data were generated under Defra-funded work to assess the potential areas of acid and nitrogen sensitive habitats at risk of adverse impacts from excess atmospheric acid and nitrogen deposition. Reducing the area and amount of critical load exceedance continues to be a driver of Government policy on reducing emissions of acidic and nitrogen-containing air pollutants (sulphur dioxide, nitrogen oxides and ammonia). Full details about this dataset can be found at https://doi.org/10.5285/049b5fd2-3f12-48fc-8b43-57fa4db649ae

  • This dataset consists of landscape and agricultural management archetypes (1 km resolution) at three levels, defined by different opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. The unavailability of several input variables for agricultural management prevented the generation of Tier 3 archetypes for Scotland. The archetypes were derived by data-driven machine learning. The three tiers of archetypes were analysed separately and not as a nested structure (i.e. a single Tier 3 archetype can occur in more than one Tier 2 archetype), predominantly to ensure that archetype definitions were easily interpreted across tiers. Full details about this dataset can be found at https://doi.org/10.5285/3b44375a-cbe6-468c-9395-41471054d0f3

  • Gridded hydrological model river flow estimates on a 1km grid over Great Britain for the period Dec 1980 - Nov 2011. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept) and annual minima of 7-day mean river flow (years spanning Dec-Nov) (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment; https://ukscape.ceh.ac.uk/, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award. Full details about this dataset can be found at https://doi.org/10.5285/2f835517-253e-4697-b774-ab6ff2c0d3da

  • This web map service provides a 1km resolution gridded coverage of wooded areas in riparian zones (river- or streamsides) across Great Britain. The areas classified as riparian in this dataset are defined by a 50 metre buffer applied to the CEH 1:50000 watercourse network. Wooded areas within this zone are identified as those classified by the Land Cover Map of Great Britain 2007 as either coniferous or deciduous woodland. The data are aggregated to a 1km resolution.

  • Dataset contains the Land Use/Land Cover (LULC) map under four scenarios (Trend, Expansion, Sustainability, and Conservation) in 2030 in the Luanhe River Basin (LRB), China, with a resolution of 1km. The scenarios were based on different socio-economic development and environmental protection targets, local plans and policies, and the information from a stakeholders’ workshop, to explore land system evolution trajectories of the LRB and major challenges that the river basin may face in the future. The map includes nine different land use classes: 1) Extensive cropland, 2) Medium intensive cropland, 3) Intensive cropland, 4) Forest, 5) Grassland with low livestock, 6) Grassland with high livestock, 7) Water, 8) Built-up area and 9) Unused land. The land system classification is based on three main classification factors: (1) land use and cover, (2) livestock, and (3) agricultural intensity. The data was funded by UK Research and Innovation (UKRI) through the Natural Environment Research Council’s (NERC) Towards a Sustainable Earth (TaSE) programme, for the project “River basins as ‘living laboratories’ for achieving sustainable development goals across national and sub-national scales” (Grant no. NE/S012427/1) . Full details about this dataset can be found at https://doi.org/10.5285/a94640dc-fe21-4c38-936b-d62dfca0c952

  • The leaf phenology product presented here shows the amplitude of annual cycles observed in MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) 16-day time-series of 2000 to 2013 for Meso- and South America. The values given represent a conservative measure of the amplitude after the annual cycle was identified and tested for significance by means of the Lomb-Scargle Transform. The amplitude was derived for four sets of vegtation indices (VI) time-series based on the MODIS VI products (500m MOD13A1; 1000m MOD13A2). The amplitude value can be interpreted as the degree in which the life cycles of individual leaves of plants observed within a pixel are synchronised. In other words, given the local variation in environment and climate and the diversity of species leaf life cycle strategies, an image pixel will represent vegetation communities behaving between two extremes: * well synchronized, where the leaf bud burst and senescence of the individual plants within the pixel occurs near simultaneously, yielding a high amplitude value. Often this matches with an area of low species diversity (e.g. arable land) or with areas where the growth of all plants is controlled by the same driver (e.g. precipitation). * poorly synchronized, where the leaf bud burst and senescence of individual plants within a pixel occurs at different times of the year, yielding a low amplitude value. Often this matches with an area of high species diversity and/or where several drivers could be controlling growth. Full details about this dataset can be found at https://doi.org/10.5285/dae416b4-3762-45bd-ae14-c554883d482c

  • This is a theoretical model of leadership in warfare by exploitative individuals who reap the benefits of conflict while avoiding the costs. In this model we extend the classic hawk-dove model to consider pairwise interactions between groups in which a randomly chosen leader decides whether the group will collectively adopt aggressive or peaceful tactics. We allow for unequal sharing of fitness payoffs among group members such that the leader can obtain either a larger share of the benefits, or pay a reduced share of costs, from fighting compared to their followers. Our model shows that leadership of this kind can explain the evolution of severe collective violence in certain animal societies. Full details about this application can be found at https://doi.org/10.5285/7aab999e-cef9-41c2-8400-63f10af798ec

  • [This dataset is embargoed until March 22, 2023]. This dataset contains time series observations of land surface-atmosphere exchanges of net ecosystem carbon dioxide exchange (NEE), sensible heat (H) and latent heat (LE), methane (CH4) and meteorological observations measured at two locations in the Flow Country blanket bog complex, Caithness and Sutherland, UK. The tower over the area affected by wildfire after initial felling (UK-DKF) is located at 58.431, -3.96 and monitors across a footprint affected by a wildfire in early May 2019. The tower over the area felled for restoration after previous afforestation (UK-DKE-RESTORED, which was not affected by the wildfire) is located at 58.428, -3.967 and measures fluxes from a footprint identical in ground flora, topography, soil type and previous management to that of the FIRE tower, with the only exception that the fire did not reach as far as the footprint monitored. The dataset comprises eddy covariance CO2, water and energy fluxes, originally collected at 20Hz and processed to 30-minute data, as well as accompanying meteorological observations, originally collected at 15 min and processed to 30-minute data. The time period covered in this dataset is 25/09/2019 – 20/05/2021. Full details about this dataset can be found at https://doi.org/10.5285/d4a7ca90-0c62-4a31-986e-f433e1644bf3

  • This is a dataset obtained from analysis of lake sediment and overlying water from six sites along a depth gradient, in Loch Leven, Scotland, over a period of one year. Parameters measured from the water and included in the dataset are dissolved oxygen concentration, conductivity, pH, temperature, concentrations of three forms of phosphorus, and ammonium and silica concentrations. Chlorophyll a concentration measured from the sediment surface is included, and within the sediment concentrations of seven different forms of phosphorus are provided. Full details about this dataset can be found at https://doi.org/10.5285/76a2bd9a-fb02-4f37-91b9-4835eb31ab7b

  • This information product contains gridded estimates of Ellenberg vegetation indicator scores for four different indicators: fertility (N); pH/reactivity (R); light availability (L) and moisture (F) at 1km2 resolution. Both cover-weighted (cwt) and non-cover weighted (site) Ellenberg indicators are estimated. Estimates are made for two different time periods, 1990 and 2015-2019 and the change between the two time periods is also presented. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/0a9900f2-8556-4487-bc13-9c2fdc05082c