These data comprise collection records of Heliconius butterfly samples collected in the Chocó-Darien ecoregion between the Andes and the Pacific in Ecuador and Colombia, and the Pacific coast of the Darien region of Panama. Samples were collected over five sampling trips between 2014 and 2016. Data were collected for a study of clinal variation across this region in Heliconius erato and Heliconius melpomene, so focus on these two species. However, in most cases all observed Heliconius species were collected. The dataset includes photographs of the wings of most of the specimens, which were used for an analysis of colour and pattern variation. Many of these individuals also have genomic information available for them on the European Nucleotide Archive (ENA) - the data includes ENA accession numbers. Data were collected as part of a NERC fellowship project (NE/K008498/1). Full details about this dataset can be found at https://doi.org/10.5285/cb23c552-caee-4221-bdd3-83b172139ae1

The data comprise summary statistics for performance of a genotyping microarray for a test set of 87 samples for four pine species. The summary statistics comprise state (polymorphic, monomorphic), mean allele frequency and conversion rate, estimated for each locus as a mean across 87 sample genotypes. The array comprised 49,829 SNPs (single nucleotide polymorphisms) from several sources. The majority (N = 49,052) were obtained from transcriptome sequencing of four pine species: Pinus sylvestris, Pinus mugo, Pinus uncinata and Pinus uliginosa. The SNP set was filtered by the array manufacturer (Thermo Fisher) based on p-convert values signifying the SNP array quality, and a list of recommended and non-recommended SNP probes (avoiding SNPs with polymorphisms within 35 bp) was provided to the authors. These included SNPs that were common to all species and also SNPs fixed in one species and polymorphic within and among others. A further set of SNPs (N = 578) were included from candidate genes (N = 279), which had been resequenced in previous population genetic studies of the pine species. Variation in mitochondrial DNA (mtDNA) was targeted by inclusion of a set of mtDNA- specific SNPs (N = 14). Finally, a set of SNPs putatively associated with susceptibility to Dothistroma needle blight (discovered in Pinus radiata, European Nucleotide Archive accession numbers ERS1034542-53) were also included (N = 185). Full details about this dataset can be found at https://doi.org/10.5285/0ba33e96-67cb-4650-b2bd-6ee13fa7de97

The dataset contains a stratified survey of ecological and soil states at sites where fine scale patterns of covariation between vegetation and edaphic characteristics were recorded. Key data collection included leaf area index, moss and organic matter thickness, surface and deeper soil moisture. Data were collected at sites in the Yukon (2013) and Northwest Territories (2014), Canada. Full details about this dataset can be found at https://doi.org/10.5285/36f4e380-d01d-44a7-8321-7a677e6996b2

The data consists of potential activity of enzymes released by microorganisms in soils of old growth forests in the Central Amazon. Soils were sampled in January 2018, seven months after nutrient addition in a large-scale nutrient fertilisation experiment. Enzyme activities are given as a mean of the microbial community per plot, where five points inside each plot were sampled in the 0-5 cm and 5-10 cm soil layers. The dataset depicts the potential activities of the following enzymes: ß-glucosidase, N-acetyl ß-glucosaminidase and phosphomonoesterase at two different soil depths. Samples were collected at the AFEX project area in Manaus, Brazil at the Biological Dynamics of Forest Fragments Project (BDFFP/ INPA). The study was funded by NERC, BDFFP (logistical support) and Brazilian government (student scholarship). Full details about this dataset can be found at https://doi.org/10.5285/b9a2523e-09ba-43c7-acfa-a894a231b133

This dataset consists of the 1km raster, percentage aggregate class version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The 1km percentage product provides the percentage cover for each of 10 aggregated land cover classes for 1km x 1km pixels. This product contains one band per aggregated habitat class (producing a 10 band image). The 10 aggregate classes are groupings of the 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UK CEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/1278d7b5-da47-46b5-b1a6-049e726425a7

[THIS DATASET HAS BEEN WITHDRAWN]. The Land Cover Map of Great Britain 1990 (1km percentage target class, GB), is a raster digital dataset, providing a classification of land cover types into 25 classes, at a 1km resolution. The dataset consists of a set of 1km bands, each containing one of 25 target classes (or 'sub' classes). Each band of the dataset contains the percentage of the specified habitat class per 1km, derived from a higher resolution (25m) dataset. The map was produced using supervised maximum likelihood classifications of Landsat 5 Thematic Mapper satellite data. The 25 mapped classes include sea and inland waters, bare, suburban and urban areas, arable farmland, pastures and meadows, rough grass, grass heaths and moors, bracken, dwarf shrub heaths and moorland, scrub, deciduous and evergreen woodland, and upland and lowland bogs. It can potentially be used to plan, manage or monitor agriculture, ecology, conservation, forestry, environmental assessment, water supplies, urban spread, transport, telecommunications, recreation and mineral extraction. The map was produced in the early 1990s by a forerunner of the Centre for Ecology & Hydrology, the Institute of Terrestrial Ecology, at Monks Wood. Note: The bands in the dataset run from 1-26, not 0-25 as stated in the documentation. Hence '1' is unclassifed (not '0'), '2' is sea/estuary and so on. Full details about this dataset can be found at https://doi.org/10.5285/0172cc8c-8b5c-46cf-b08a-785ab832e88c

Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 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 , incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Product consists of 1km percentages per Target Class. Northern Ireland only. Full details about this dataset can be found at https://doi.org/10.5285/e611794a-2f7c-4cfc-a8ab-4c38131e0fad

[THIS DATASET HAS BEEN WITHDRAWN]. 1km resolution gridded meteorological variables over Great Britain for the years 1961-2012. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-1) and air pressure (Pa), plus daily temperature range (K). These are the variables required to run the JULES land surface model [1] with daily disaggregation. The precipitation data were obtained by scaling the Gridded estimates of daily and monthly areal rainfall (CEH-GEAR) daily rainfall estimates [2,3] to the units required for JULES input. Other variables were interpolated from coarser resolution datasets, taking into account topographic information. [1] Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes, Geoscientific Model Development, 4, 677-699, doi:10.5194/gmd-4-677-2011, 2011. [2] 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 doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e [3] Keller,V. D. J., Tanguy, M. , Prosdocimi, I. , Terry, J. A. , Hitt, O., Cole, S. J. , Fry, M., Morris, D. G., Dixon, H. (2015) CEH-GEAR: 1km resolution daily and monthly areal rainfall estimates for the UK for hydrological use. Earth Syst. Sci. Data Discuss., 8, 83-112, www.earth-syst-sci-data-discuss.net/8/83/2015/ doi:10.5194/essdd-8-83-2015. Full details about this dataset can be found at https://doi.org/10.5285/80887755-1426-4dab-a4a6-250919d5020c

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 2017. 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/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556

The dataset contains daily and monthly surface water, energy and carbon fluxes, and state variables for Great Britain over the period between 1961 and 2015. The data was obtained from a 55 years simulation with the JULES Land Surface Model, at 1 km spatial resolution and driven by the meteorological dataset CHESS-met v1.2 (Robinson et al., 2017; https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900). The data comes in both monthly (all variables) and daily (only variables with no z dimension) averages. The variables are: total evapotranspiration and components (kg m-2 s-1), runoff (kg m-2 s-1), surface temperature (K), soil moisture (kg m-2), soil temperature (K), snow mass (kg m-2). latent and sensible heat (W m-2), net and gross primary productivities (kg C m-2 s-1), plant respiration (kg C m-2 s-1). The z dimension may refer, if present, to tile (surface type), pft (plant functional type) or soil (soil layer). This simulation forms the basis for new research paper by Blyth et al (2017, under review). Full details about this dataset can be found at https://doi.org/10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096