NERC Data Catalogue Service
NERC Data Catalogue Service (Natural Environment Research Council)
Continents, countries, sea regions of the world.
UK Environmental Change Network (ECN) Fine Grain Vegetation data: 1994-2012
[THIS DATASET HAS BEEN WITHDRAWN]. Fine Grain Vegetation data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected at all of ECN's terrestrial sites using a standard protocol (see supporting documentation). In this protocol, 10m x 10m plots are randomly selected within each vegetation type on the site - species presence is recorded in 40cm x 40 cm cells randomly selected within these plots. They represent continuous records every three years (some sites record data annually) from 1992 to 2012. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage. Full details about this dataset can be found at https://doi.org/10.5285/5a1efdfc-8f51-4ee9-9ed4-677bddd3f135
If you reuse this data, you should cite: Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N., Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R., Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N., Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Eaton, E., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K., Watson, H., Whittaker, M. (2016). UK Environmental Change Network (ECN) Fine Grain Vegetation data: 1994-2012 . NERC Environmental Information Data Centre https://doi.org/10.5285/5a1efdfc-8f51-4ee9-9ed4-677bddd3f135
Data are collected at all of ECN's terrestrial sites using a standard protocol (http://www.ecn.ac.uk/measurements/terrestrial/v). In this protocol 10m x 10m plots are randomly selected within each vegetation type on the site - species presence is recorded in 40cm x 40 cm cells randomly selected within these plots. Prior to deposit of the data in the EIDC, data are managed by the ECN Data Centre at CEH Lancaster according to defined protocols. Verification steps include numeric range checks (i.e. checking if a value falls within a specified range), categorical checks (e.g. checking that a species code appears on the standard code list), formatting (i.e. that the dataset conforms to the specified data format) and logical integrity checks (i.e. checking the data make sense, e.g. that the dates in one dataset match those in a related dataset). Appropriate range settings for ECN variables have been selected following discussion with specialists in each field. Where data fall outside these ranges, a cautious approach has been adopted towards discarding data on the principle that apparent errors may be valid outliers. Such values are discarded only if there is a clear explanation (e.g. an instrumentation error) and corrections are made where possible. If the reason is unclear, the values are stored, but are qualified using pre-defined quality codes or free-text descriptions. Data providers also use these codes or free text to describe factors affecting sampling outside their control, instrument damage or site management effects.
Trait responses of aquatic organisms from microfibre exposure studies published 2015-2024 v2
The resource includes data extracted from published laboratory studies investigating the impacts of microfibres (fibres < 5 mm in size) on various traits within aquatic organisms. Data were accessible on 19/06/2024 and represent studies from 2015 to 2024. The dataset includes measured responses in a range of aquatic organisms after microfibre exposure. Measures correspond to the following defined traits: 1. Development-Healthy molting and growth of organs and structures. 2. Fecundity-Number of offspring and quality of gametes. 3. Feeding-Intake, handling and egestion of resources. 4. Growth-Change in organism weight, length or derived measures. 5. Hatching -Number, emergence time and morphology of offspring. 6. Non-apical endpoints-Indices and measures of general organism wellbeing. 7. Survival-Organisms alive at a particular time point. This dataset was created to collate microfibre study findings for meta-analyses and also to identify gaps within the literature. This work was supported by NERC (NE/Y003985/1) as part of the Future Fibres Networking Grant. This dataset is a corrected version of https://doi.org/10.5285/e4d566bf-b571-460e-816e-0efa1ab3335a Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/8b7fcc6c-ac73-4f28-b82f-ea818b064915
If you reuse this data, you should cite: Parker, B., Lewis, C., Galloway, T. (2025). Trait responses of aquatic organisms from microfibre exposure studies published 2015-2024 v2. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/8b7fcc6c-ac73-4f28-b82f-ea818b064915
This resource is available under the terms of the Open Government Licence
The dataset was generated from an initial Web of Science database search of studies investigating the exposure impacts of microfibres (small fibres < 5 mm in size) on the responses of aquatic organisms. Web of Science results were evaluated and then the citing and cited studies from these results also screened. Finally, the compiled results were then assessed for eligibility as detailed in the supporting information and only eligible responses extracted. QA steps were used to exclude a number of studies and responses as outlined in the resource supporting information. This dataset is a corrected version of https://doi.org/10.5285/e4d566bf-b571-460e-816e-0efa1ab3335a which had several incorrectly calculated standard exposures and consequently incorrect assignment to standard exposure categories. This new version includes details on how these standard exposures were calculated as well as including more details on the assignment of several categories. Finally, "Health" endpoints are now referred to as "non-apical endpoints".
Aquatic carbon and greenhouse gas concentrations in the Auchencorth Moss catchment following drain blocking
This dataset contains concentrations of dissolved organic carbon, inorganic carbon, CO2, CH4 and N2O in the Black Burn stream which drains Auchencorth Moss peatland in South East Scotland. Concentrations and fluxes have been measured within the Black Burn on an approximately weekly to fortnightly basis from approximately 2006 to present (see https://doi.org/10.5285/3f0820a7-a8c8-4dd7-a058-8db79ba9c7fe). Concentrations in this dataset are from a series of new sites, upstream of the long-term sampling record, adjacent to an area of drains blocked by Scottish Natural Heritage. Measurements began during the drain blocking. Data was collected initially as part of a masters project for University of Edinburgh through Scotland's Rural College and continued by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/88ffbf44-0ec0-41d6-9814-04bc3535cd84
© Database Right/Copyright Scottish Natural Heritage
If you reuse this data, you should cite: Dinsmore, K.J., Murphey, O., Leith, F., Carfrae, J. (2016). Aquatic carbon and greenhouse gas concentrations in the Auchencorth Moss catchment following drain blocking. NERC Environmental Information Data Centre https://doi.org/10.5285/88ffbf44-0ec0-41d6-9814-04bc3535cd84
Registration is required to access this data
The study was set up by Dr Kerry Dinsmore. Water and headspace (for gas analysis) samples were collected primarily by Orla Murphey and Dr Fraser Leith. Analysis was carried out at CEH Edinburgh, again primarily by Orla Murphey and Dr Fraser Leith. Field equipment was installed by Orla Murphey and Dr Kerry Dinsmore and maintained by Orla Murphey and Dr Fraser Leith. Further detail is provided in the supporting documentation.
Annual Forest Loss at 15m resolution in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2006-2014
The dataset delineates annual forest loss in the CAZ digitized from annual 15 metre resolution LANDSAT imagery. This dataset creates a baseline for possible long-term, near real-time monitoring of encroachment and illegal logging within the Ankeniheny-Zahamena Corridor (CAZ), Madagascar. This analysis includes a summary of suspected encroachment and illegal logging from June 2006 to December 2010 within the "protected area," which consists of zones such as priority for conservation, sustainable use, and controlled settlements. Full details about this dataset can be found at https://doi.org/10.5285/c63c543f-3e95-4c1c-8c69-12f942271813
If you reuse this data, you should cite: Cano, C.A. (2017). Annual Forest Loss at 15m resolution in the Corridor Ankeniheny-Zahamena (CAZ), Madagascar, 2006-2014. NERC Environmental Information Data Centre https://doi.org/10.5285/c63c543f-3e95-4c1c-8c69-12f942271813
This resource is made available under the terms of the Open Government Licence
Landsat 7 SLC-off imagery at a resolution of 15 m was compared to Landsat 2005 imagery and the Change in Natural Forest Cover from 1990 to 2005 developed by the United States Agency for International Development (USAID), Conservation International and the Madagascar Ministry of Environment, Water, Forests and Tourism. The imagery was acquired from the U.S. Geological Survey (USGS) Global Visualization Viewer (Glovis), available at http://glovis.usgs.gov/. ENVI 4.7 was used for processing and gap filling of the imagery. Visual interpretation based on shape, color, proximity to features and texture was used to digitize suspected forest encroachment using ArcGIS 9.3.1. The data have not been validated; therefore some of the clearings are likely due to natural disturbances such as landslides, flooding, natural fires or tree falls.
Land Cover Map 2022 (10m classified pixels, N. Ireland)
This is a 10m pixel data set representing the land surface of Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a two-band raster in GeoTiff format. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover. A full description of this and all UKCEH LCM2022 products are available from the LCM2022 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/c2d1e87f-1fa8-4421-82a7-df960b4b5821
If you reuse this data, you should cite: Marston, C.G., Morton, R.D., O'Neil, A.W., Rowland, C.S. (2024). Land Cover Map 2022 (10m classified pixels, N. Ireland). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/c2d1e87f-1fa8-4421-82a7-df960b4b5821
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2019 to 2021. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra. Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2022 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation. The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
Simulated 15-min discharge time-series and baseline simulations for the River Kent following a Natural Flood Management intervention of 'Enhanced Hillslope Storage', Sedgewick, October 2015 to January 2016
Simulated 15-min discharge time-series (1/10/2015-17/1/2016) for the River Kent at Sedgwick following a Natural Flood Management intervention of ‘Enhanced Hillslope Storage’ plus the baseline simulations are presented. To derive these data, the observed 15-minute discharge River Kent measured at the Environment Agency (EA) Sedgwick gauging station (https://nrfa.ceh.ac.uk/data/station/info/73005) through the 1 Oct 2015 to 17 Jan 2016 period were modelled using the latest version of Lancaster University’s Dynamic TOPMODEL (https://cran.r-project.org/web//packages/dynatop/index.html). The spatially distributed rainfall field used as input to TOPMODEL was derived from a new direction-dependent and topographically controlled interpolation using observed rainfall data for the Cumbrian Mountains (Page et al., 2022. Hydrological Processes 36: e14758, https://doi.org/10.1002/hyp.14758). Lack of perfect understanding of the hydrological processes routing rainfall for stream channels and then along stream channels to the Sedgwick gauge was represented by using a very wide range of model parameters applied randomly within 10,000 simulations. Using the approach detailed in Beven et al. (2022a. Hydrological Processes 36(10): e14703, https://doi.org/10.1002/hyp.14703), the resultant wide range of simulated discharge time-series was reduced by rejecting all but 67 simulations that passed the prescribed criteria. These 67 baseline simulations of observed behaviour through the +3 month period at Sedgwick are presented here. To represent the effect of adding surface storage distributed across this 209 sq km River Kent catchment, the Digital Elevation Model (DEM) used in the baseline simulations according to Hankin et al (2018. Technical report SC150005/R6. Environment Agency, Bristol. 77pp, https://www.gov.uk/flood-and-coastal-erosion-risk-management-research-reports/working-with-natural-processes-to-reduce-flood-risk) to represent bunds placed on hillslopes in rural areas. The bunds are a type of flood mitigation measure known as Natural Flood Management or NFM. These are known formally as ‘Enhanced Hillslope Storage’ or EHS features (Beven et al 2022b. Hydrological Processes 36: e14752, https://doi.org/10.1002/hyp.14752). The TOPMODEL parameter sets producing the 67 ‘acceptable’ baseline simulations were then re-run with the modified DEM. These results are also presented here. Full details about this dataset can be found at https://doi.org/10.5285/af081a90-b014-43f7-9399-c948a8b7672f
If you reuse this data, you should cite: Chappell, N.A., Beven, K.J., Smith, P., Hankin, B. (2024). Simulated 15-min discharge time-series and baseline simulations for the River Kent following a Natural Flood Management intervention of 'Enhanced Hillslope Storage', Sedgewick, October 2015 to January 2016. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/af081a90-b014-43f7-9399-c948a8b7672f
Creative Commons Attribution 4.0 International (CC BY 4.0)
The simulations were performed with the latest version of Lancaster University’s distributed catchment model called ‘Dynamic TOPMODEL’. Version 0.2.1 and this is publicly available at: https://cran.r-project.org/web//packages/dynatop/index.html Dynamic TOPMODEL was used to assess change in flood peaks associated with Natural Flood Management (NFM) by refining the baseline results according to a Condition Tree Approach, where the first step is the choice of model, in this case Dynamic TOPMODEL. The second step is establishing a prior range of model parameters (see Table 1 in Beven et al., 2022, https://doi.org/10.1002/hyp.14752). The third step is the initial sampling strategy of Monte Carlo sampling of prior parameter distributions assuming independent uniform distributions. This gave 100,000 simulated river discharge datasets for the before (baseline) and post-intervention (Enhanced Hillslope Storage) simulations. The fourth step was to apply the Limits of Acceptability criteria determined from 2.5 to 97.5 quantiles of distributions of runoff coefficients from past events. These were applied to ranked hydrograph time steps for period in 2015 (Table 1 in Beven et al., 2022b, https://doi.org/10.1002/hyp.14703) including the largest flood on record, with additional timing constraint of ± 2 h. This gave 3249 surviving river discharge datasets for each scenario. The fifth step was to undertake additional evaluation of flood peaks, where Limits of acceptability approach was applied ranked hydrograph time steps in 2005 and 2009 (as set out in Table 2 in Beven et al., 2022, https://doi.org/10.1002/hyp.14752). This gave 118 surviving river discharge datasets for each scenario. Lastly, step six, the valuation of areas producing saturation excess overland flow was applied. Here simulations producing less than 10% of the area with surface storage greater than 1 mm at any time step additionally rejected. This gave 67 surviving river discharge datasets for each scenario. These 67 results are those presented in this shared dataset. For the NFM scenario, the location of the Enhanced Hillslope Storage features across the modelled River Kent catchment was based on the publically available WwNP (Working with Natural Processes) dataset (Hankin et al., 2018 Technical report SC150005/R6. Environment Agency, Bristol. 77pp, https://www.gov.uk/flood-and-coastal-erosion-risk-management-research-reports/working-with-natural-processes-to-reduce-flood-risk). For the simulations presented in this NFM-scenario dataset, each bund feature was modelled with a residence time of 10 hours.
The EMEP4UK modelled daily atmospheric composition
The European Monitoring and Evaluation Program Unified Model for the UK (EMEP4UK) simulates the year 2001 to 2014 UK daily average atmospheric composition at a horizontal resolution of 5 x 5 km2. The species included in this dataset are surface daily average concentrations of: nitric oxide (NO), nitrogen dioxide (NO2), particulate matter (PM10 and PM2.5), ammonia (NH3), nitric acid (HNO3), sulphur dioxide (SO2), ammonium (NH4+), nitrate (NO3-), sulphate (SO42-), PM2.5 organic matter, and ground level ozone (O3). The EMEP4UK model framework consists of an atmospheric chemistry transport model (ACTM) which simulates hourly to annual average atmospheric composition and deposition of various pollutants and the weather research and forecast model (WRF). Pollutants simulated include fine particulate matter (PM10, PM2.5), secondary organic aerosols (SOA), elemental carbon (EC), secondary inorganic aerosols (SIA), sulphur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOx), and ground level ozone (O3). Dry and wet deposition of pollutants are also generated by the EMEP4UK. WRF is used to calculate the required meteorological input data for the ACTM. The version of EMEP4UK used to model the 2001-2014 dataset available here is based on the EMEP Meteorological Synthesizing Centre West (MSC-W) model version rv4.4. A version of this dataset, calculated using the EMEP model version rv4.17 and the WRF model version 3.7.1 is available at https://doi.org/10.5285/b0545f67-e47c-4077-bf3c-c5ffcd6b72c8 Notes: Only the simulations for the years between 2002-2012 include data for from forest fire. The emissions used for simulating the years 2013 and 2014 are the same as the year 2012 (updated date will be made available as soon as 2013 and 2014 national emission inventory data have been processed). The calculated year 2001-2012 use a different version of the WRF model, moreover for the year 2013 and 2014 the WRF model setup was changed as the specific Humidity is no longer nudged with re-analysis in the WRF simulation. Acknowledgements required (third-party datasets used to drive the model): The WRF model calculated meteorology uses the dataset from NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. (http://www.wrf-model.org/). National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2000, updated daily. NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. (http://doi.org/10.5065/D6M043C6). Emissions data from the EMEP emissions center (www.emep.int), and NAEI web site (http://naei.defra.gov.uk).
The UK Automatic Urban and Rural monitoring Network (AURN) hourly data are used to verify the EMEP4UK model output: Vieno, M., Dore, A. J., Stevenson, D. S., Doherty, R., Heal, M. R., Reis, S., Hallsworth, S., Tarrason, L., Wind, P., Fowler, D., Simpson, D., and Sutton, M. A.: Modelling surface ozone during the 2003 heat-wave in the UK, Atmospheric Chemistry and Physics, 10, 7963-7978 https://doi.org/10.5194/acp-10-7963-2010, 2010 Vieno, M., Heal, M. R., Williams, M. L., Carnell, E. J., Nemitz, E., Stedman, J. R., and Reis, S.: The sensitivities of emissions reductions for the mitigation of UK pm2.5, Atmos. Chem. Phys., 16, 265-276 https://doi.org/10.5194/acp-16-265-2016, 2016. The UK Acid Gases and Aerosols Network (AGANet) monthy data are also used to verify the EMEP4UK model output: Vieno, M., Heal, M. R., Hallsworth, S., Famulari, D., Doherty, R. M., Dore, A. J., Tang, Y. S., Braban, C. F., Leaver, D., Sutton, M. A., and Reis, S.: The role of long-range transport and domestic emissions in determining atmospheric secondary inorganic particle concentrations across the UK, Atmos. Chem. Phys., 14, 8435-8447 https://doi.org/10.5194/acp-14-8435-2014, 2014 The EMEP4UK model have been evaluated against other models in the Defra organised model inter-comparison: Carslaw, D. C.: Report: Defra deposition model evaluation analysis - Phase 1, London, http://uk-air.defra.gov.uk/library/reports?report_id=652, access 01/04/2015, 2011. Carslaw, D. C.: Report: Defra regional and transboundary model evaluation analysis - Phase 1, London, http://uk-air.defra.gov.uk/library/reports?report_id=653, access 01/04/2015, 2011. The EMEP MSC-W from which the EMEP4UK model is derived is routinely updated and validated in EU: www.emep.int No further QA/QC is carried out.
Malaysian tropical heath forest growth and chemistry after fertilisation, 2016-2018
The dataset consists of tree diameter at breast height (DBH) of all trees > 1 cm DBH in 16 parcels of 15 m x 15 m in the tropical heath forest of the Kabili-Sepilok Forest Reserve (Sabah, Malaysia). Also included are the tropical heath forest trees’ leaf element concentration. Tree DBH has been measured three times, one before (2016) and two after (2017 and 2018) the experimental application of Nitrogen fertiliser and CaCO3 to the forest floor. The leaves were collected before (2016) and after (January 2017; July 2017; June 2018) the application of experimental nitrogen and CaCO3 fertilisation. The experimental setting consists of four replicate parcels for the control plots, four replicates for the Nitrogen treatment, four replicates for the CaCO3 treatment and four replicates the Nitrogen and CaCO3 treatments combined. Full details about this dataset can be found at https://doi.org/10.5285/d5ea9e2c-f053-4631-aead-a8a5557488c6
If you reuse this data, you should cite: Sellan, G., Thompson, J., Majalap-Lee, N., Brearley, F.Q. (2021). Malaysian tropical heath forest growth and chemistry after fertilisation, 2016-2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/d5ea9e2c-f053-4631-aead-a8a5557488c6
All data were generated by the authors. Trees were identified at species level (where possible) by expert botanists and unidentified species were sorted into morphospecies. To assure high quality of element concentrations we included certified reference materials and each sample was measured three times. We also ensured that the measured values fit within those of calibration standards and, if necessary, re-analysed samples diluted to fall within the calibration range. All calculations were checked for errors before data was accepted.
Tree trait, insect herbivore abundance and powdery mildew infection data (2016-17) from Forest Research Climate Matching Trials, UK
This data set comprises two years of data (2016 and 2017) from one trial (Hucking, Kent, UK) and one year (2017) from a second trial (Hartshorne, Derbyshire, UK). Data was collected on tree traits (tree height, shoot length, tree provenance), abundance of foliar insect herbivores (gallers, leaf manipulators and leaf miners) and leaf damage by oak powdery mildew, a foliar fungal pathogen. Data was collected from plots differing in tree diversity (provenance and species diversity). Full details about this dataset can be found at https://doi.org/10.5285/cbccb101-c877-4e43-ac70-e8a852b51f07
If you reuse this data, you should cite: Field, E., Schönrogge, K., Barsoum, N., Hector, A., Gibbs, M. (2019). Tree trait, insect herbivore abundance and powdery mildew infection data (2016-17) from Forest Research Climate Matching Trials, UK. NERC Environmental Information Data Centre https://doi.org/10.5285/cbccb101-c877-4e43-ac70-e8a852b51f07
The study took place at the Climate Match tree diversity experiment planted in 2011 at two sites in the UK. One site was established in South East England at Hucking, Kent (51°17'47.5"N 0°37'58.2"E), the other in the East Midlands at Hartshorne, Derbyshire (52°47'41.1"N, 1°30'58.8"W). On focal Quercus robur trees, we recorded abundance of three insect guilds: gallers, leaf miners and leaf manipulators, as well as infection by oak powdery mildew. We also recorded information on tree traits (tree height, shoot length). For further information on the design of the trials and the sampling protocol, see the supporting metadata for the dataset.
Taxonomic data, brain, and neuropil volume measurements of Ithomiini butterflies, Orellana Province, Ecuador, 2011-2012
This dataset presents taxonomic data, and neuropil volume measurements of sensory structures in the brains of wild caught ithomiini butterflies (Lepidoptera, Nymphalidae, Danainae), within a single community in Yasuní national park, Orellana Province, Ecuador, collected in 2011-2012. Variables measured include taxonomic detail, sex, mimicry ring, and the volumes of major sensory neuropils (the medulla, lobula plate, lobula, ventral lobula, accessory medulla, antennal lobe and anterior optic tubercule) and the central brain volume. This dataset was created to examine how sensory environments shape investment in sensory brain centers in butterflies, as part of a NERC Independent Research Fellowship NE/N014936/1. Full details about this dataset can be found at https://doi.org/10.5285/8056bba1-8370-4d2b-b537-018c79c8acb0
If you reuse this data, you should cite: Montgomery, S., Wainwright, B.J. (2024). Taxonomic data, brain, and neuropil volume measurements of Ithomiini butterflies, Orellana Province, Ecuador, 2011-2012. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/8056bba1-8370-4d2b-b537-018c79c8acb0
Samples were collected over two field seasons in 2011/12 in Yasuní National Park, Ecuador, fixed in situ and returned to the UK. Fixed samples were then stained with Anti-SYNORF as a primary antibody, cy2 as a secondary antibody, and imaged using confocal microscopy. The resulting images, which highlight boundaries between brain regions, were then segmented using AMIRA software to quantify neuropil volumes. Please see supporting documentation for further detail.
NERC EDS Environmental Information Data Centre
Plynlimon research catchments: Severn elevation contour lines
Elevation contour lines within the Severn catchment at 10 metre intervals. Digitised from the scanned topographic maps.
The contour lines were digitised from the following scanned topographic map: Plynlimon Catchment Areas - Severn Catchment. Sheet No. 1. Scale 1:5000. Wallingford, Berkshire. Institute of Hydrology, Natural Environmental Research Council, 1968. Natural Environmental Research Council. The contours were recorded as a series of points by Edinburgh University. They were converted into lines by CEH using ArcGIS 10 geo-processing tools.
Historic Standardised Precipitation Index time series for IHU Groups (1862-2015)
[THIS DATASET HAS BEEN WITHDRAWN]. 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 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Standardised Precipitation Index time series for IHU Groups (1961-2012)' [SPI_IHU_groups] (Tanguy et al., 2015 [3]), apart from the temporal extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Keller et al., 2015 [4], Tanguy et al., 2014 [5]) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). Within Historic Droughts project (grant number: NE/L01016X/1), the Met Office has digitised historic rainfall and temperature data to produce high quality historic rainfall and temperature grids, which motivated the change in the underlying data to calculate SPI. The methodology to calculate SPI is the same in the two datasets. [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. [3] Tanguy, M.; Kral., F.; Fry, M.; Svensson, C.; Hannaford, J. (2015). Standardised Precipitation Index time series for Integrated Hydrological Units Groups (1961-2012). NERC Environmental Information Data Centre. https://doi.org/10.5285/dfd59438-2170-4472-b810-bab33a83d09f [4] Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H.: CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use, Earth Syst. Sci. Data Discuss., 8, 83-112, doi:10.5194/essdd-8-83-2015, 2015. [5] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2014). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2012) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e Full details about this dataset can be found at https://doi.org/10.5285/047d914f-2a65-4e9c-b191-09abf57423db
If you reuse this data, you should cite: Tanguy, M., Fry, M., Svensson, C., Hannaford, J. (2017). Historic Standardised Precipitation Index time series for IHU Groups (1862-2015). NERC Environmental Information Data Centre https://doi.org/10.5285/047d914f-2a65-4e9c-b191-09abf57423db
SPI is calculated as originally defined in McKee et al. [1]. SPI is based on the cumulative probability of a given rainfall amount occurring at a location. The historic rainfall data of the station is fitted to a statistical distribution. For this dataset, the statistical distribution used is the gamma distribution, which has been extensively used and is recommended as a default choice for Europe by Stagge et al. [2]. The L-moments method was used to estimate the gamma distribution parameters, as the maximum likehood method was failing to fit a realistic distribution in some isolated cases (more details on the choice of methodology in Tanguy et al. [3]). To calculate SPI, the R package SCI was used (Gudmundsson & Stagge [4]), but modified to use L-moments (instead of Maximum Likelihood). The input data used is the monthly rainfall grids from the Met Office 5km gridded rainfall product provided within Historic Droughts project (grant number: NE/L01016X/1), which has been area-averaged over each of the Integrated Hydrological Units (IHU) Hydrometric Areas (Kral et al. [5]). [1] 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. [2] Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F. and Stahl, K. (2015), Candidate Distributions for Climatological Drought Indices (SPI and SPEI). Int. J. Climatol. doi: 10.1002/joc.4267 [3] Tanguy et al. (2015), in preparation. [4] Gudmundsson, L. & Stagge, J. H. (2014). Package 'SCI': Standardized Climate Indices such as SPI, SRI or SPEI. Repository CRAN, http://cran.r-project.org/web/packages/SCI/SCI.pdf [5] Kral, F., Fry, M., Dixon, H. (2015). Integrated Hydrological Units of the United Kingdom: Hydrometric Areas without Coastline. NERC-Environmental Information Data Centre doi:10.5285/3a4e94fc-4c68-47eb-a217-adee2a6b02b3
UK Environmental Change Network (ECN) baseline vegetation data: 1991-2000
Baseline Vegetation data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected at all of ECN's terrestrial sites using a standard protocol (see supporting documentation). This was a one-off whole site baseline vegetation survey (though given the intensive nature of this survey, some sites did it over successive years) to generate a vegetation map and identify the areas within the site to be monitored. In this protocol up to 500 systemic 2m x 2m plots were surveyed and species presence recorded. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage. Full details about this dataset can be found at https://doi.org/10.5285/a7b49ac1-24f5-406e-ac8f-3d05fb583e3b
If you reuse this data, you should cite: Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N., Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R., Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N., Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Eaton, E., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K., Watson, H., Whittaker, M. (2016). UK Environmental Change Network (ECN) baseline vegetation data: 1991-2000. NERC Environmental Information Data Centre https://doi.org/10.5285/a7b49ac1-24f5-406e-ac8f-3d05fb583e3b
Data are collected at all of ECN's terrestrial sites using a standard protocol (http://www.ecn.ac.uk/measurements/terrestrial/v). This was a one-off whole site baseline vegetation survey (though given the intensive nature of this survey some sites did it over successive years) to generate a vegetation map and identify the areas within the site to be monitored. In this protocol up to 500 systemic 2m x 2m plots were surveyed and species presence recorded. Prior to deposit of the data in the EIDC, data are managed by the ECN Data Centre at CEH Lancaster according to defined protocols. Verification steps include numeric range checks (i.e. checking if a value falls within a specified range), categorical checks (e.g. checking that a species code appears on the standard code list), formatting (i.e. that the dataset conforms to the specified data format) and logical integrity checks (i.e. checking the data make sense, e.g. that the dates in one dataset match those in a related dataset). Appropriate range settings for ECN variables have been selected following discussion with specialists in each field. Where data fall outside these ranges, a cautious approach has been adopted towards discarding data on the principle that apparent errors may be valid outliers. Such values are discarded only if there is a clear explanation (e.g. an instrumentation error) and corrections are made where possible. If the reason is unclear, the values are stored, but are qualified using pre-defined quality codes or free-text descriptions. Data providers also use these codes or free text to describe factors affecting sampling outside their control, instrument damage or site management effects.
Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]
1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2019. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. Full details about this dataset can be found at https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c
© Database Right/Copyright UK Centre for Ecology & Hydrology. All rights reserved.
Contains material based on Met Éireann data © Met Éireann and Met Office data © Crown copyright.
Users should be aware that the quality of the rainfall data will decrease when the minimum distance (i.e. distance to the closest gauge) increases. The value of the minimum distance is provided alongside rainfall values within the dataset. For values of minimum distance greater than 100 km, rainfall values are not derived.
If you reuse this data, you should cite: Tanguy, M., Dixon, H., Prosdocimi, I., Morris, D.G., Keller, V.D.J. (2021). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c
The rainfall estimates are derived from the Met Office national database of observed precipitations. To derive the monthly estimates, monthly and daily (when complete month available) precipitation totals from the UK raingauge network are used. An additional quality control step (data already quality controlled by the Met Office) was introduced to check events greater than the 200 year return period. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (SAAR61-90), was used to generate the daily and monthly rainfall grids. The daily grids are adjusted so that the monthly totals calculated from those are in agreement with the monthly grids. As an indicator of the quality of the estimates, the dataset also contains for each grid generated a grid containing the distance to the closest raingauge used to interpolate the grid point. A grid containing the number of missing estimates for each year is also provided. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. This version of the CEH-GEAR dataset differs from the previous version (1890-2017) only by temporal coverage. That is, previous years' data have not been modified.
Visible leaf injury of Trifolium repens versus Lolium perenne after ozone exposure in solardomes
The dataset consists of proportions of ozone injured or senesced leaves from a study which investigated how the presence of competing species in a community affects these two common responses to ozone. Monocultures and mixtures of Trifolium repens and Lolium perenne were grown in large containers and were exposed in solardomes to either a rural episodic ozone profile (AOT40 (Accumulated Ozone Threshold exposure of 40 parts per billion) of 12.86 ppm h) or control conditions (AOT40 of 0.02 ppm h) for 12 weeks. The proportion of ozone-injured or senesced leaves was determined in different regions of the canopy, the upper canopy (>14cm high), the canopy edge and the inner canopy, by separating injured/senesced leaves from healthy leaves. The experiment was carried out at the CEH Bangor Air Pollution Facility. This work was funded by the Centre for Ecology & Hydrology Integrating Fund Initiative Full details about this dataset can be found at https://doi.org/10.5285/bc4d0325-b67b-4fff-a14b-6e06edf397bd
If you reuse this data, you should cite: Hayes, F., Mills, G., Ashmore, M. (2014). Visible leaf injury of Trifolium repens versus Lolium perenne after ozone exposure in solardomes. NERC Environmental Information Data Centre https://doi.org/10.5285/bc4d0325-b67b-4fff-a14b-6e06edf397bd
This study has investigated how the presence of competing species in a community affects two common responses to ozone: visible injury and senescence. Monocultures and mixtures of Trifolium repens and Lolium perenne were grown in large containers and were exposed in solardomes to either a rural episodic ozone profile (AOT40 of 12.86 ppm h) or control conditions (AOT40 of 0.02 ppm h) for 12 weeks. The proportion of ozone-injured or senesced leaves was different in the different regions of the canopy. The highest proportions of injured/senesced leaves were in the plant material growing at the edge of the canopy and the upper canopy, with a significantly lower proportion of injured leaves in the inner canopy. The presence of L. perenne increased the proportion of ozone-injured leaves in T. repens at the final harvest, whilst the presence of T. repens decreased the proportion of senesced leaves in L. perenne. In L. perenne, the proportion of injured leaves at the edge and inner canopy decreased significantly when grown in competition, whilst for T. repens the reverse effect occurred in the inner canopy only. Different mechanisms appeared to influence the interaction between response to ozone and competitors in these two species. In L. perenne the response to ozone may have been related to nitrogen supply, whereas in T. repens canopy structure was more important.
Surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a data from Windermere South Basin, UK, 2024
This is part of an ongoing long-term monitoring dataset of surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a from fortnightly sampling at the South Basin of Windermere in Cumbria, England, that began in 1945 for some variables. The data have been collected by the UK Centre for Ecology & Hydrology (UKCEH). The data available to download comprise surface temperature (TEMP) in degree Celsius, surface oxygen saturation (OXYG) in % air-saturation, Secchi depth (SECC) in metres, alkalinity (ALKA) in µg per litre as CaCO3 and pH. Total ammoniacal nitrogen (NH4N), total oxidised nitrogen (TON), soluble reactive phosphate (PO4P), total phosphorus (TOTP), dissolved reactive silicon expressed as SiO2 (SIO2) and phytoplankton chlorophyll a (TOCA) are all given in µg per litre. Water samples are based on a sample integrated from 0 to 7m. Water chemistry values below the detection limit, are indicated by a < sign in the column titled ‘sign(if<LOD)’. Measurements are made from a boat at a marked location (buoy) at the deepest part of the lake. All data are from January 2024 until the end of 2024. Full details about this dataset can be found at https://doi.org/10.5285/a3cb5212-d174-400f-9775-4e2f699e91e3
If you reuse this data, you should cite: Feuchtmayr, H., Carter, H.T., Dodd, B.A., Keenan, P.O., Le Quesne, K., McShane, G., Moorhouse, H., Rankin, G., Rhodes, G., Thackeray, S.J. (2025). Surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a data from Windermere South Basin, UK, 2024. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/a3cb5212-d174-400f-9775-4e2f699e91e3
Please note that the data presented here are raw data that have not yet been quality controlled and calibrated. However, data for TEMP, OXYG, SECC, ALKA, pH, PO4P and TOCA has been double entered by different people into excel from field notebooks or laboratory paper record sheets and validated by a database manager. The database manager checked all data visually (line graphs). Missing data are due to weather conditions, equipment limitations, or staff shortages.
United Kingdom Butterfly Monitoring Scheme: site location data 2017
This dataset provides the details of all sites which have been monitored as part of the UK Butterfly Monitoring Scheme (UKBMS). Data includes the location within the UK, the length and width of the line transect on each site, and how long the transect has been monitored. The UKBMS started in 1976 with fewer than 50 sites (a small number of pilot sites started from 1973). The number of sites monitored each year has increased to over a thousand since 2008. There is turnover in sites monitored each year and details of the first and last year in which each site was surveyed are given. The majority of site data is provided by recorders at the time a transect is created. The majority of these recorders are volunteers. The UK Butterfly Monitoring Scheme is organized and funded by Butterfly Conservation (BC), the Centre for Ecology & Hydrology (CEH), the British Trust for Ornithology (BTO), and the Joint Nature Conservation Committee (JNCC). The UKBMS is indebted to all volunteers who contribute data to the scheme. Full details about this dataset can be found at https://doi.org/10.5285/65396d52-3e27-499e-a1c6-b22b653d62ad
Attribution statement: Contains UK Butterfly Monitoring Scheme (UKBMS) data © copyright and database right Butterfly Conservation, the Centre for Ecology & Hydrology, British Trust for Ornithology, and the Joint Nature Conservation Committee.
If you reuse this data, you should cite: Botham, M.S., Brereton, T., Harris, S., Harrower, C., Middlebrook, I., Randle, Z., Roy, D.B. (2019). United Kingdom Butterfly Monitoring Scheme: site location data 2017. NERC Environmental Information Data Centre https://doi.org/10.5285/65396d52-3e27-499e-a1c6-b22b653d62ad
Site location data provide details about the linear transect at each UKBMS site. Data on the location and length and width of each transect are provided by the recorder for each site. Data on how long each transect has been monitored is calculated as the time period between when a transect was first and last monitored.
Plant growth parameters of Bengal and Assam Aus Panel (BAAP) rice cultivar seedlings under contrasting nitrogen treatments
The data report plant growth parameters measured on six week old rice plants grown under two contrasting nitrogen treatments. The purpose is to conduct genome wide association mapping of nitrogen response and uses 229 cultivars of the Bengal and Assam Aus Panel. Traits are plant height, shoot dry weight, tiller number and two measures from a Dualex hand-held meter, the nitrogen balance index and the chlorophyl index. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/7ac2ba22-6915-43ca-93cc-d75b9c458b51
If you reuse this data, you should cite: Price, A., Norton, G., Hazzazi, Y., Tantray, A. (2023). Plant growth parameters of Bengal and Assam Aus Panel (BAAP) rice cultivar seedlings under contrasting nitrogen treatments. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/7ac2ba22-6915-43ca-93cc-d75b9c458b51
Plants were grown in 8 large boxes of soil each containing one replicate of 229 plants and 600 g of local soil. There were two treatments, zero nitrogen and 100% nitrogen being 16.12 g of urea replicates and four replicates. Plants were grown under LED lights in a controlled environment growth room. At six weeks a Dualex hand held meter was used to assess chlorophyl content and nitrogen balance index. Plant height and tiller number were recorded and then the shoots were cut, dried for two days and shoot dry weight measures with a balance.