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.

The data contains urination metrics including frequency, volume, chemical composition, estimated urine patch N loading rates and metabolomics profile of individual urine events from sheep (Welsh Mountain ewe) grazing a semi-improved upland pasture and a lowland improved pasture located in North Wales, UK. Urine collection studies were run in the spring, summer and autumn of 2016 for the semi-improved site and in autumn of 2016 on the lowland improved pasture. Sheep were housed in urine collection pens and while in the pens, each individual urine event was collected and stored separately. The study was conducted as a wider part of the NERC funded Uplands-N2O project (Grant No: NE/M015351/1). The frequency, volume and chemical composition of individual urine events has implications for nitrogen losses from the grazed pasture ecosystem, including emissions of the powerful greenhouse gas, nitrous oxide, and nitrate leaching. Full details about this dataset can be found at https://doi.org/10.5285/385ec5ab-0c47-46fc-b5df-008ca024296f

Estimates of in-river concentrations (mg/l) and loads (kg/day) of nutrients to rivers in England and Wales from multiple sector sources, modelled with SAGIS (Source Apportionment GIS). The nutrients include nitrate (mg/l N) and ortho-phosphate (mg/l P); the estimate loads are expressed as kilograms per day (kg/day) and the in-river concentrations as milligrams per litre (mg/l). Sources are both diffuse and point. Diffuse sources include livestock farming, arable farming, highways, urban runoff, background (from soils), onsite wastewater treatment systems and atmospheric deposition. Point sources include treated wastewater effluent, combined sewer overflows and storm tanks, industrial discharges and mine water discharges. Concentrations and loads are modelled using the Environment Agency's catchment river model, SIMCAT, at the locations of model features or every 1 km along each river, taking into account all upstream sources and user defined river losses. SAGIS is a modelling framework was developed through the UK Water Industry Research Programme (UKWIR) project 'Chemical Source Apportionment under the WFD' [1], with support from the Environment Agency and SEPA. The model is also described in [2] [1] UKWIR (2012) Chemical Source Apportionment under the WFD (12/WW/02/3). Final report for UK Water Industry Research, 1 Queen Annes Gate, London, ISBN: 1 84057 637 5. [2] Comber, S.D.; Smith, R.; Daldorph, P.; Gardner, M.J.; Constantino, C.; Ellor, B. (2013) Development of a Chemical Source Apportionment Decision Support Framework for Catchment Management. Environ. Sci. Technol. 47, 9824-9832 Full details about this dataset can be found at https://doi.org/10.5285/8c5d9e38-0244-4a39-8600-a85513a6fecf

This dataset contains instream dissolved oxygen data collected continuously at one minute intervals for five sites in the Hampshire Avon catchment in the United Kingdom. Data were collected between August 2014 and August 2015 using miniDOT loggers. Full details about this dataset can be found at https://doi.org/10.5285/840228a7-40a1-4db4-aef0-a9fea2079987

This dataset consists of change data for areas of Broad Habitats across Great Britain between 1990 and 1998, between 1990 and 2007, and between 1998 and 2007. The data are national estimates generated by analysing the sample data from up to 591 1km squares and scaling up to a national level. The data are summarized as change in habitat area per Land Class (areas of similar environmental characteristics). The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB and using the 'ITE Land Classification' as a method of stratification. The data were collected as part of Countryside Survey, a unique study or 'audit' of the natural resources of the UK's countryside. The Survey has been carried out at regular intervals since 1978 by the Centre for Ecology & Hydrology. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 with repeated visits to the majority of squares. In addition to habitat areas, vegetation species data, soil data, linear habitat data, and freshwater habitat data are also gathered by Countryside Survey. Full details about this dataset can be found at https://doi.org/10.5285/7e2981e7-bd4c-4992-b7b0-1b1253bfd20d

The database of chemical composition of Central Asian forage plants contains just under 1000 desert and steppe species with information such as Latin and Russian names and family and related records of chemical composition from various sources including percentages by weight of protein, ash, cellulose and fat. Where available, it also includes data on digestible protein content, metabolisable energy and Soviet Feed Units (SFU). Records also include information on the country, location, season or month and phenological phase at time of collection of each sample. As one of the original uses of the database was for modelling food and energy intake by the saiga antelope, it also includes information identifying saiga food plant species along with sources of this information. Data on the edibility of many species for livestock in different seasons are also available. See the detailed documentation available here for more information on the data types, definitions and sources. NB The database is in text format and must be imported e.g. into relational database software, as Unicode (UTF-8) in order to convert the Cyrillic characters in Russian names. Full details about this dataset can be found at https://doi.org/10.5285/6a5a9a2a-730b-49f7-9e42-2295040aee56

A species by quadrat matrix showing the percentage cover of understory herbaceous plants in ancient and recent woodlands varying in age and isolation. Percentage cover was calculated for each species individually. The data was collected on the Isle of Wight, woodlands spanned the entire island and were not situated in one area. All data was collected in the summer of 2021 over a period of 3 weeks covering the last 2 weeks of may May and the first week of June. This time was chosen as this is when a large subset of woodland plants are in flower. Woodlands were sampled in blocks of three, each block contains an ancient woodland, a recent woodland adjacent to the ancient woodland and another recent woodland of similar age and size but isolated from the ancient woodland. Each woodland had six quadrats taken, systematically placed at four corners and two in the centre. Within each quadrat the percentage cover of all understory herbs was recorded. This data was collected to measure the colonisation credit of recently planted woodlands, and to observe how much this might vary under differing degrees of isolation. This data could also be used to compare all sorts of biodiversity metrics between connected and isolated recent woodlands. It could also be used to compare beta diversity metrics between woodlands of varying degrees of isolation Full details about this dataset can be found at https://doi.org/10.5285/7c2b2878-1d15-4ddd-9d7e-cf50bd65f652

These spatial layers contain risk factors and overall risk scores, representing relative risk of Phytophthora infection (Phytophthora ramorum and P. kernoviae), for heathland fragments across Scotland. Risk factors include climate suitability, proximity to road and river networks and suitability of habitat for key hosts of Phytophthora and were broadly concurrent with the period between 2007 and 2013. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). Full details about this dataset can be found at https://doi.org/10.5285/8f09b7e6-6daa-4823-b338-4edad8de1461

The dataset contains water chemistry data collected from peatland headwaters across the Flow Country following a wildfire in May 2019. Samples were collected on a monthly basis from 52 sites across the region from September 2019 to October 2020. Sampling sites were selected to represent peatland catchments in the following conditions: burned near natural, burned drained, unburned near natural, unburned drained and unburned forested. Data were obtained via collection of water samples in situ, and concentrations were derived via subsequent sample processing and analysis. Full details about this dataset can be found at https://doi.org/10.5285/57748e4f-d0a4-4648-8a61-bd1c2066db1e

Data comprise scores (from 0 to 5) of examples of cultural ecosystem services provided by cockles from Portugal, Spain, France, Ireland and the UK. All data were collected using an a priori framework to classify evidenced examples of services during a face-to-face workshop held in Vigo in north-west Spain, 10th April 2018, with 28 participants from eleven organisations. The workshop was followed up over the following months by smaller country-specific meetings, mostly held by teleconference call or video call and by email. The data were collected as part of a research and industry collaboration, under the COCKLES project ‘Co-operation for restoring cockle shellfisheries and its ecosystem services in the Atlantic Area’, co-funded through the European Regional Development Fund (ERDF). Full details about this dataset can be found at https://doi.org/10.5285/a924f41c-ae29-427c-8113-aebe6bc2d349