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  • This dataset is a compilation of results obtained from vegetation surveys in the Stalybride estate moorlands (commonly known as the Saddleworth moors) following a wildfire in 2018. Ten plots were established in October 2018 at the post-fire site which were 10 m x 10 m in size. Five plots were identified as suffering a less severe (shallow) burn. The other 5 plots were in areas where a more severe (deep) burn. In all plots the surface vegetation had been removed by the fire exposing the bare peat. The data file contains: (1) On-site post-fire vegetation data – species ID and coverage, and (2) species presence in the one-year post-fire seed bank. The dataset is the result of research in the light of an NERC Urgency grant entitled 'RECOUP-Moor: Restoring Ecosystem CarbOn Uptake of Post-fire Moorland' (NE/S011943/1, led by Dr. Bjorn Robroek of the University of Southampton (now Radboud University Nijmegen, the Netherlands). Full details about this dataset can be found at https://doi.org/10.5285/56561ed3-55d0-454c-a6b9-7e633ccf9647

  • Bird data comprises point counts of bird species and their abundance observed at each collection site. Data are separated into birds within 50m, greater than 50m distant and birds in flight. These data were collected from the South Fork McKenzie river, Oregon, USA in June 2021 following the Holiday Farm wildfire in Autumn 2020. Samples were collected from a restored and unrestored reach of the South Fork McKenzie River with a view to quantifying differences in avian response to wildfire in the restored vs. unrestored river reaches. The study was conducted by the University of Nottingham, with data collected by partners from The US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/dd919c8e-ccd6-48ed-a1c0-ef5cf732bdc6

  • Vegetation survey data comprise per-quadrat species level data and abundances, abundance cover classes (following Braun-Blanquet method), family, growth duration, habitat and native species. Data also contain ground cover class and Denisom reading for tree canopy cover. Data were collected from the South Fork McKenzie river, Oregon, USA in June 2021 following the Holiday Farm wildfire in Autumn 2020. Vegetation surveys were conducted in restored and unrestored reaches of the South Fork McKenzie River with a view to quantifying differences in vegetation response to wildfire in the restored vs. unrestored river reaches. The study was conducted by the University of Nottingham, with data collected by partners from The US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/251081d0-0388-44fa-b5f9-a4c784f64218

  • Data comprise macroinvertebrate count data (identified to species level), trait and classification information, as well as information on macroinvertebrate biomass and site-specific observations (e.g. canopy cover, habitat type, etc.) collected from the South Fork McKenzie river, Oregon, USA in Autumn 2019 and 2020, and winter of 2021 following the Holiday Farm wildfire in Autumn 2020. Samples were collected from restored and unrestored river reaches to quantify the difference in the response of benthic macroinvertebrate response to wildfire. The study was conducted by the University of Nottingham, with data collected by partners from the US Forest Service, Portland State University, Washington State University and Colorado State University. The work was funded by the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/50119e9c-b6d9-4b72-98c9-588ca1d7c6fe

  • Soil data comprises sample depth, moisture content, % sand/silt/clay, texture, and various nitrate/nitrite/carbon metrics. These data were collected from the South Fork McKenzie River, Oregon, USA in July 2020, February 2021 and June 2021 following the Holiday Farm wildfire in Autumn 2020. Samples were collected from a restored and unrestored reach of the South Fork McKenzie River with a view to quantifying differences in soil response to wildfire in the restored vs. unrestored river reaches. The study was conducted by the University of Nottingham, with data collected by partners from The US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council grant NE/V021443/1. Full details about this dataset can be found at https://doi.org/10.5285/d69e854a-f01d-4d5e-8819-219053e8d00c

  • This dataset consists of a single orthophoto mosaic image of Irontongue Hill on Swineshaw Moor. The area of interest includes seven erosion plots (approximately 5 x 5 m) which were set up on 26/07/2018 to capture the state of the burnt moorland surface and monitor subsequent erosion and vegetation recovery. The area of interest is approximately 0.45 km2. Full details about this dataset can be found at https://doi.org/10.5285/aff5210d-27e9-4655-badb-4d16c3adeb17

  • This dataset contains biogeochemical and edaphic information from burned peat soil on the Stalybridge estate located near Manchester (UK), commonly referred to as Saddleworth moor. This study was conducted after a wildfire fire on the Saddleworth moor in June 2018. The sample plots included areas with deep and shallow peat burn. The data includes geographical information (location, elevation and slope), soil temperature and soil chemical composition (carbon, nitrogen and 22 other elements). The dataset is the result of research funded by a NERC Urgency grant entitled 'RECOUP-Moor: Restoring Ecosystem CarbOn Uptake of Post-fire Moorland' (NE/S011943/1, led by Dr. Bjorn Robroek of the University of Southampton (now Radboud University Nijmegen, the Netherlands). Full details about this dataset can be found at https://doi.org/10.5285/1fa8d605-b996-4687-ace2-1fa59cd5c6dd

  • Periphyton data consists of diatom scrubs sampled in a range of riffle and pool habitats including diatom taxa counts (identified to genus level) and computed autotrophic index (ratio of the organic mass per cm2 to mass (microgram) of chlorophyll a) as well as site characterisation data. The data were collected from the South Fork McKenzie river, Oregon, USA in September of 2021 and February 2022 following the Holiday Farm wildfire in Autumn 2020. Samples were collected from a restored and unrestored reach of the South Fork McKenzie River with a view to quantifying differences in periphyton response to wildfire in the restored vs. unrestored river reaches. The study was conducted by the University of Nottingham, with data collected by partners from The US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/6b7337fa-037b-4c03-a8be-fd4c5722fe1a

  • The data comprise Sentinel-2 derived burn severity rasters covering restored and unrestored reaches of the South Fork McKenzie river, Oregon USA. The data were collected in order to quantify differences in burn severity in restored and unrestored river reaches following the Holiday Farm wildfire in 2020. Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground. The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd

  • The data presented here comprise a catalogue of 61736 camera trap images obtained during the period June - November 2020 (this period is described within the dataset as 'setup 1'). Following the explosion at the Chornobyl nuclear power plant in April 1986, a 5000 km2 exclusion zone surrounding the plant was created; people and farm animals were subsequently evacuated from the area. In April 2020 there were severe wildfires within the Ukrainian part of the exclusion zone (2600 km2) where approximately 870 km2 was burnt. The NERC-funded CHAR project conducted a study which involved placing motion activated digital camera traps at three sites (each covering an area of 80 km2) within the Ukrainian exclusion zone from June 2020 - August 2021 to assess large mammal activity following the fire. Thirteen cameras were randomly located at each site; all camera deployment locations had been used in a previous study 2014-2015 (https://tree.ceh.ac.uk/content/chernobyl-webcams). All the images obtained during June - November 2020 are included as part of the dataset with the exception of those images containing people, vehicles or members of the CHAR research team setting up and servicing the cameras; these images have been catalogued but they are not included in the dataset to protect privacy. Information on camera deployment periods, site characteristics and descriptions of each camera location (e.g. geographic coordinates, estimates of ambient dose rate, description of animal trails or tracks and the extent of fire damage in vicinity of where the camera is mounted) have also been included as part of the dataset. Staff from the Chornobyl Center for Nuclear Safety deployed, maintained and downloaded information from the cameras and provided field notes and observations of habitat. UKCEH staff populated the dataset using the information provided. Full details about this dataset can be found at https://doi.org/10.5285/9bd7754d-ea87-4b35-bec1-f39d5cc76db6