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This dataset contains 3D Lidar scans representative for 0.5 ha permanent sample plots at Caatinga, Brazil. Two plots were located in Serra das Almas Reserve (SDA) and one plot in Petrolina (PET). The dataset also includes scans completed inside and outside for 10 individual trees. Scans were taken between July 2017 and May 2019. Full details about this dataset can be found at https://doi.org/10.5285/6a29d55e-b40e-4f8c-8dd2-5cf0e27634ac
Elevation contour lines within the Wye catchment at 10 and 20 metre intervals. The contour lines have been digitised from a scanned topographic map.
This web map shows positive plant habitat condition indicators across Great Britain (GB). This data provides a metric of plant diversity weighted by the species that you would expect and desire to have in a particular habitat type so indicates habitat condition. In each Countryside Survey 2007 area vegetation plot the number of positive plant habitat indicators (taken from a list created from Common Standards Monitoring Guidance and consultation with the Botanical society of the British Isles (BSBI)) for the habitat type in which the plot is located are counted. This count is then divided by the possible indicators for that habitat type (and multiplied by 100) to get a percentage value. This is extrapolated to 1km squares across GB using a generalised additive mixed model. Co-variables used in the model are Broad Habitat (the dominant broad habitat of the 1km square), air temperature, nitrogen deposition, sulphur deposition, precipitation and whether the plot is located in a Site of Special Scientific Interest (SSSI) (presence or absence data).
The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30′ E-119°45′ E, 39°10′ N-42°40′ N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012; (3) the flood inundation prediction under different development strategies and climate scenarios to 2030; (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168
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 data was collected from co-evolving bacterial populations containing Pseudomonas fluorescens strain SBW25 and a plasmid, pQBR57. The composition of the community was tracked using flow cytometry to distinguish 1) an unlabelled wild type strain 2) a dTomato compensated host (SBW25 KO PFLU4242), and 3) a wild type host bearing a Green fluorescent protein (GFP) labelled compensated plasmid (pQBR57 KO 0059). Full details about this dataset can be found at https://doi.org/10.5285/51046841-deaa-422f-a303-2c0759f014b4
This dataset includes key photosynthesis and respiration data collected from three common garden sites along an elevation/temperature gradient in the Colombian Andes. Raw A-Ci data, the Vcmax (carboxylation of RuBP by the enzyme Rubisco) and Jmax (the regeneration of RuBP by the electron transport chain) values estimated from this data, and Rdark (leaf dark respiration) values collected using spot measurements, are all available, along with variables such as leaf temperature (°C), relative humidity (%) and pressure values (kPa) returned by the LI-6800 portable photosynthesis system. Full details about this dataset can be found at https://doi.org/10.5285/60dd0b8f-f0c3-4e30-841b-1c2067052974
This dataset contains a mosquito species table with counts for adults and larvae. Samples are from 12 UK wetland sites, sampled between April 2017 and September 2018. A map included in the wetlandmetadata.docx file shows site locations, which include both coastal and inland wetlands, and range from Devon to Kent and from Lincolnshire to Dorset. Samples were collected by staff from University of Greenwich and the UK Health Security Agency: collaborators in a NERC-funded project (NE/NO13379/1), part of the Valuing Nature Programme. We found a total of 19 mosquito species: • 10 Aedes • 3 Anopheles • 3 Culisseta • 2 Culex • 1 Coquillettidia. Full details about this dataset can be found at https://doi.org/10.5285/1b5a8827-4329-4c20-85f8-0979998b3c74
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 HAS BEEN WITHDRAWN]. Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al. ). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. . 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 ), 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 , Tanguy et al., 2014 ) 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.  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  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.  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  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.  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