This dataset consists of ecology data from 16 paired field sites; each pair consisting of an organic and conventional farm. A multiscale sampling design was employed to assess the impact of (i) location-within-field (field margin vs. edge vs. centre), (ii) crop type (arable cereal vs. permanent pasture), (iii) farm management (organic vs. conventional) and (iv) landscape-scale management (landscapes that contained low or high fractions of organic land) on a wide range of taxa. Studied taxa include birds, insect pollinators (hoverflies, bumblebees and solitary bees), epigeal arthropods, aphids and their natural enemies, earthworms and plants. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761. Soil data from agricultural land under differing crop and management regimes,are also available. Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Northern Ireland. The raster consists of 27 bands. Within each band, each 1km pixel represents a percentage cover value for one of 27 target (or 'sub') classes, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Note that the Band numberings in the dataset run from 1-27 rather than 0-26 and therefore each band relates to the one below it in the subclass code list (i.e. 1 = Unclassified, labelled as 0 in the list). Full details about this dataset can be found at https://doi.org/10.5285/8eed6d77-714a-438a-aa65-887b1ef62378

[This dataset is embargoed until May 1, 2025]. The dataset contains information on six functional traits of woody plants, including Leaf Area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Thickness (Lth), Wood Density (WD), Bark Thickness (BT). It also includes data on concentrations of C, N, P, Ca, Mg, and K in leaves; leaf fresh mass and leaf dry mass data; and fresh wood volume and dry wood mass data to calculate Wood Density. Ten leaves per individual were sampled, and three measurements were taken for leaf thickness at the base, middle, and top of the leaf. For wood density, a single branch sample was taken per individual and for bark thickness five measurements per individual were made. The data were collected between 2019 and 2022 from 27 forest monitoring plots (0.5 ha each) in five locations along an altitudinal (lowland, mid-elevation, and highland forests) and forest perturbation (low, medium, and high perturbation levels) gradient in Andean ecosystems in Colombia. The database includes information about the plot location and parameters of the locality. The purpose of this data collection was to determine whether the expression of functional traits in woody plants differs between the perturbation gradient and the relationship of the traits to ecosystem processes. This information is important for understanding the drivers of variation in forest resilience and the impacts of perturbation on ecosystem functioning. This data set was obtained within the framework of the BioResilience project, a transdisciplinary investigation that seeks to understand the resilience of forest ecosystems after the post-conflict period in Colombia. Full details about this dataset can be found at https://doi.org/10.5285/0da218a8-2882-4ee7-bb2a-50a51f7f4138

This dataset contains prey items of common guillemot Uria aalge and razorbill Alca torda observed during the 2018 breeding season at East Caithness Special Protection Area (SPA), Buchan Ness to Collieston Coast SPA and Isle of May National Nature Reserve, off the east coast of Scotland. Diet of these two species has been studied on the Isle of May since the 1980s (Harris & Wanless 1985, 1986; Wilson et al 2004; Daunt et al. 2008; Thaxter et al 2013). To our knowledge, only two previous studies of diet has been undertaken at Buchan Ness to Collieston Coast SPA (in 2006, 6km to the north of the site used in this study; Anderson et al. 2014; and in 2017, using a similar protocol as in 2018; Daunt et al. 2017), and one previous study of diet has been undertaken at East Caithness SPA (2017; Daunt et al. 2017). Full details about this dataset can be found at https://doi.org/10.5285/d7164910-17cb-44cd-bccd-6a9c31b6ed70

This dataset consists of a 1km resolution raster version of the Land Cover Map 2007 for Great Britain. Each 1km pixel represents the dominant aggregate class across the 1km area. The aggregate classes are aggregations of the target classes, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2007. LCM2007 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2007 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/c3723adb-1a8c-4b57-958b-1d610d2c37fe

Gridded potential evapotranspiration calculated from HadUK-Grid gridded observed meteorological data at 1 km resolution over the United Kingdom for the years 1969-2021. This dataset contains two potential evapotranspiration variables: daily total potential evapotranspiration (PET; kg m-2 d-1) and daily total potential evapotranspiration with interception correction (PETI; kg m-2 d-1). The units kg m-2 d-1 are equivalent to mm d-1. The data are provided in gridded netCDF files. There is one file for each variable, for each calendar month. These data were generated as part of NERC grant NE/S017380/1 (Hydro-JULES: Next generation land surface and hydrological prediction.) Full details about this dataset can be found at https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb

Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al. [1]). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [2]. SPI is calculated for different accumulation periods: 1, 3, 6, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1961 to 2012. [1] Kral, F., Fry, M., Dixon, H. (2015). Integrated Hydrological Units of the United Kingdom: Groups. NERC-Environmental Information Data Centre doi:10.5285/f1cd5e33-2633-4304-bbc2-b8d34711d902 [2] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. Full details about this dataset can be found at https://doi.org/10.5285/dfd59438-2170-4472-b810-bab33a83d09f

[THIS DATASET HAS BEEN WITHDRAWN]. Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible , incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Dataset consists of the dominant Target Class per 1km square, raster dataset (Great Britain only). Full details about this dataset can be found at https://doi.org/10.5285/337f9dea-726e-40c7-9f9b-e269911c9db6

[This dataset is embargoed until May 1, 2025]. This dataset contains measurements of soil pyrogenic carbon, ratio of %PyC to %Bulk Carbon and organic carbon, which were collected in a soil fertility gradient in the Amazon Basin. All samples were taken in old-growth forests. In total, 49 forest plots were sampled and analysed for PyC soil concentration, representing 395 soil samples. Full details about this dataset can be found at https://doi.org/10.5285/6410a578-d21a-4285-8e9c-57efbe2b60d5

This dataset consists of palaeoecological measurements taken at sites in the Peak District and NW Sutherland during the NERC Rural Economy and Land Use (RELU) programme. This data collection includes the results from four interlinked projects combining quantitative and qualitative evidence to assess long-term ecological data at local to national levels: Project 1 synthesises existing information on historical environmental changes in the uplands with relevance to current management and policy Project 2 used high resolution palaeoenvironmental analyses to reconstruct ecological changes and land-use histories of four contrasting moorland systems in the Peak District (England) over the last c.200-1300 yrs. Sites were selected in consultation with stakeholders and the results provide the basis for comparison with ecological survey results and knowledge of current managers. Project 3 used similar methods to reconstruct ecological and land-use changes in NW Sutherland (Scotland) over the last c.400 yrs. Site selection was based on discussion with stakeholders and results were compared with stakeholder knowledge and preferences for landscape change. Project 4 used three choice experiments to assess the response of different communities to long-term evidence as a potential source of information to inform preferences for upland management. Project 4a used a choice experiment to assess the influence of long-term evidence on management preferences of residents of the Peak District. Project 4b used choice experiments to present long-term evidence to ecologists from government, NGO, research and practitioner communities in conjunction with established sources of ecological evidence used in upland management (ecological monitoring and ecological research) and with stakeholder preferences for upland management, since this is increasingly becoming embedded in decision-making. The upland woods and peatlands were used as the contexts for two choice experiments. This dataset consists of palaeoecological measurements taken at sites in the Peak District and NW Sutherland, as part of projects 2 and 3 as listed above. The choice experiment data from this study are available at the UK Data Archive under study number 6791 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).