This dataset contains urine frequency and volume data measured from tri-axial accelerometers on Welsh mountain ewes free-grazing two contrasting upland field sites (semi-improved and unimproved pasture) in North Wales, across two seasons each (spring and autumn). The data, were collected using tri-axial accelerometers glued to the hind of Welsh Mountain ewes to study the urination behaviour of free-grazing sheep. Using a Boolean algorithm, the characteristic squatting position that ewes exhibit upon urination was detected in the accelerometer data. Initially the performance of the accelerometers with sheep in urine collection pens was assessed. Data were collected on the volume of each urination event and recorded the time of each observed urination event. This initial data was used to assess whether the accelerometers and Boolean algorithm were successful in identifying urination events, but also to ascertain whether the time spent in the squatting position would correlate with the volume of urine produced (thus allowing the technique to be able to estimate urine volume from squatting time only in subsequent field deployments). Information on when, where and how often livestock urinate are key data to be able to assess the scale and nature of nitrogen pollution arising from grazed agroecosystems. Urine patches deposited by grazing livestock are large sources of emissions of the greenhouse gas, nitrous oxide, due to high concentrations of nitrogen deposited over relatively small areas. These data were collected in the NERC funded Uplands-N2O project (grant award: NE/M015351/1). Full details about this dataset can be found at https://doi.org/10.5285/127afd24-d2cd-457f-b837-2dd5d328f101

The dataset contains concentrations of total soil organic carbon, soil carbon fractions, soil CO2 fluxes, soil temperature and moisture in the Peruvian Andes. Measurements and sampling took place between 2010 and 2013. Data were generated as part of a larger NERC project: 'Are tropical uplands regional hotspots for methane and nitrous oxide' Full details about this dataset can be found at https://doi.org/10.5285/3813aef3-71cc-49e6-ba21-495a43363001

This dataset contains the gridded estimates per 1 km2 for mean and median ensemble outputs from 4-6 individual ecosystem service models for Sub-Saharan Africa, for above ground Carbon stock, firewood use, charcoal use and grazing use. Water use and supply are identically supplied as polygons. Individual model outputs are taken from previously published research. Making ensembles results in a smoothing effect whereby the individual model uncertainties are cancelled out and a signal of interest is more likely to emerge. Included ecosystem service models were: InVEST, Co$ting Nature, WaterWorld, Monetary value benefits transfer, LPJ-GUESS and Scholes models. Ensemble outputs have been normalised, therefore these ensembles project relative levels of service across the full area and can be used, for example, for optimisation or assignment of most important or sensitive areas. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. Full details about this dataset can be found at https://doi.org/10.5285/11689000-f791-4fdb-8e12-08a7d87ad75f

[This dataset is embargoed until September 1, 2024]. Vegetation data from field surveys undertaken at two experimental trials at Martin Down NNR, to investigate the potential for reducing dense Brachypodium pinnatum cover (experiment 1) and preventing further expansion of sparse cover (experiment 2). Experiment 1 explores the use of herbicide and reseeding, whilst experiment 2 examines cutting and grazing in the spring, autumn and both seasons. Percentage cover of all vascular plant species were recorded in 50 cm x 50 cm quadrats in each treatment replicate for both experiments. Surveys were undertaken in 2019 as a baseline before the experiments commenced, and post treatment in 2020, 2021 and 2022. Full details about this dataset can be found at https://doi.org/10.5285/f15e64c0-db65-40ec-8b6d-50573f5f6694

Rabbit and deer data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected by transect at ECN's terrestrial sites using a standard protocol. The protocol uses an index method based on dropping counts (of deer, rabbits - and where appropriate sheep and Grouse) to estimate relative abundance. They represent twice-yearly continuous records from 1993 to 2015. 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/10.5285/0be0aed3-f205-4f1f-a65d-84f8cfd8d50f

[THIS DATASET HAS BEEN WITHDRAWN]. Rabbit and deer data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected by transect at ECN's terrestrial sites using a standard protocol. The protocol uses an index method based on dropping counts (of deer, rabbits - and where appropriate sheep and Grouse) to estimate relative abundance. They represent twice-yearly continuous records from 1993 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/10.5285/672f5522-7917-42fc-a806-65cb5c1d2709