This service displays a series of datasets consisting of mean estimate distribution maps of ash trees (Fraxinus excelsior) across Great Britain. It includes ash trees in areas less than half a hectare, ash trees in woody linear features and individual ash trees. The data are derived from Countryside Survey 2007. Trees were mapped in 569 1km sample squares across Britain using a stratified random sampling system based on the ITE Land Classification. Mean national estimates were produced by scaling up from the sample data.

This dataset provides linear trends, over varying time periods, for the UK Butterfly Monitoring Scheme (UKBMS) Collated Indices of individual butterfly species across the UK. The main statistical values derived from a linear regression (slope, standard error, P-value) are presented for the entire time series for each species (1976 to 2011), for the last 20 years, and for the last decade. In addition a trend class, based on slope direction and its significance, and a percentage change for that time period are provided to describe the statistical trends. These trend data are provided for 59 UK butterfly species. Trends across different time series allow us to determine the long and short-term trends for individual species. This is enables us to focus conservation and research and also to assess species responses to conservation already in place. The Centre for Ecology & Hydrology (CEH) and Butterfly Conservation (BC) are responsible for the calculation and interpretation of this trend datasets. The collection of the underlying UKBMS data is reliant on a large volunteer community. The UK Butterfly Monitoring Scheme is funded by a consortium of organisations led by the Joint Nature Conservation Committee (JNCC). This dataset is updated annually and a more recent version of the UKBMS species trends (2012) is now available. Full details about this dataset can be found at https://doi.org/10.5285/cad2af6c-0c97-414c-8d5f-992741b283cf

This dataset presents taxonomic data, and neuropil volume measurements of sensory structures in the brains of wild caught ithomiini butterflies (Lepidoptera, Nymphalidae, Danainae), within a single community in Yasuní national park, Orellana Province, Ecuador, collected in 2011-2012. Variables measured include taxonomic detail, sex, mimicry ring, and the volumes of major sensory neuropils (the medulla, lobula plate, lobula, ventral lobula, accessory medulla, antennal lobe and anterior optic tubercule) and the central brain volume. This dataset was created to examine how sensory environments shape investment in sensory brain centers in butterflies, as part of a NERC Independent Research Fellowship NE/N014936/1. Full details about this dataset can be found at https://doi.org/10.5285/8056bba1-8370-4d2b-b537-018c79c8acb0

This dataset records the Saiga antelope die-off and calving sites in Kazakhstan. It represents the locations (and where available dates) of (i) die-offs and (ii) normal calving events in the Betpak-dala population of the saiga antelope, in which three major mass mortality events have been recorded since 1988. In total, the data contains 214 saiga die-off and calving sites obtained from field visits, aerial surveys, telemetry and literature. Locations derived from field data, aerial surveys or telemetry are polygons representing the actual size and shape of the die-off or calving sites; locations sourced from the literature are point data around which buffers of 6km were created, representing the average size of calving aggregations. Of the 214 locations listed, 135 sites for which environmental data were available were used to model the probability of a die-off event. The collection and use of these data are written up in more detail in papers which are currently under review (when published links will be added to this record). Saiga antelope are susceptible to mass mortality events, the most severe of which tend to be caused by haemorrhagic septicaemia following infection by the bacteria Pasteurella multocida. These die-off events tend to occur in May during calving, when saigas gather in dense aggregations which can be represented spatially as relatively small sites. The Betpak-dala population is one of three in Kazakhstan, located in the central provinces of the country (see map). Full details about this dataset can be found at https://doi.org/10.5285/8ad12782-e939-4834-830a-c89e503a298b

Records of leaf damage caused by and parasitism of Cameraria ohridella in Britain in 2010 collected with a citizen science approach as part of the Conker Tree Science citizen science project, plus validation of the data. Over 3500 people in Great Britain provided data at a national scale on an invasive insect (horse-chestnut leaf-mining moth, Cameraria ohridella Deschka & Dimic; Lepidoptera: Gracillariidae) in order to address two hypotheses. Specifically: (1) whether the levels of damage caused to leaves of the horse-chestnut tree, Aesculus hippocastanum L., and (2) whether the level of parasitism of C. ohridella larvae were both greatest where C. ohridella had been present the longest Participants recorded leaf damage on an ordinal scale (0-4) during the summer (1st July to 15th October 2010). In order to assess the levels of parasitism of caterpillars of C. orhidella, we invited people to rear insects from horse chestnut leaves infested with C. ohridella. Participants sampled leaves during the first week of July 2010 (i.e. the first of the moth's gererations that year) and stored them in sealed plastic bags for two weeks. We then asked participants to report the number of leaf-mines, and to identify and count the insects in each category: adult C. ohridella moths, parasitoids, and other insects. Anyone could take part in rearing parasitoids, but we particularly focused on school children aged 8-11 by working with a team of eight trained volunteers across the country who directly contacted schools and led lessons in classes. The volunteers did not provide directive guidance during the time that the children were counting adult moths and parasitoids, so the data were not biased by our supervision. At the completion of the activity, we retained a randomly-selected subset of 669 samples that the children had counted. We also retained an additional 75 samples in which children had reported parasitoids. For all of these samples an expert blindly assessed the counts of leaf mines, adult C. ohridella moths and other insects. In order to assess how many years that C. orhidella had been present in a location, we used a long-term dataset collated by Forest Research (used with permission). These data showed under-sampling of the range of C. orhidella after 2006, so we also modeled the predicted arrival of C. orhidella based on a demographic model of spread parameterised in continental Europe by augementing the known distribution with a model of short-distance spread by the model. We ran the model twice, assuming two and three generations of C. ohridella, respectively. The project was supported by the Natural Environment Research Council and undertaken at the University of Bristol, UK. Full details about this dataset can be found at https://doi.org/10.5285/9f913f10-6e3d-449e-b8af-8fa2d06d7fd3

[This dataset is embargoed until December 14, 2025]. This data resource contains counts of bumblebee abundance between December 2019 and March 2020 along a latitudinal gradient within Argentina and Chile. Sampling of both invasive (Bombus terrestris and B. ruderatus) and native (B. dahlbomii) bumblebee species was undertaken using standardised transect walks along a latitudinal gradient from -54.871 to -29.906 from 145 individual locations. Full details about this dataset can be found at https://doi.org/10.5285/1797a623-e582-4c0c-bc67-cd9de04e0df0

This dataset provides linear trends, over varying time periods, for the UK Butterfly Monitoring Scheme (UKBMS) collated Indices of individual butterfly species across the UK. The main statistical values derived from a linear regression (slope, standard error, P-value) are presented for the entire time series for each species (1976 to 2018), for the last 20 years, and for the last decade. In addition a trend class, based on slope direction and its significance, and a percentage change for that time period are provided to describe the statistical trends. These trend data are provided for 59 UK butterfly species. Trends across different time series allow us to determine the long and short-term trends for individual species. This enables us to focus conservation and research and also to assess species responses to conservation already in place. The UK Butterfly Monitoring Scheme is organized and funded by Butterfly Conservation (BC), the Centre for Ecology & Hydrology (CEH), the British Trust for Ornithology (BTO), and the Joint Nature Conservation Committee (JNCC). The UKBMS is indebted to all volunteers who contribute data to the scheme. 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/ee4b440e-2604-40b9-bca7-19d6392bd9ea

The speciesRecordTools R package contains functions for examining the distribution of species records, understanding sampling trends and potential biases, and building correlative presence-background species distribution models for prediction of the distribution of species across the landscape. The package is built to work with the Environmental Record Centre for Cornwall and the Isles of Scilly's (ERCCIS) opportunistic species records. Full details about this application can be found at https://doi.org/10.5285/030b49f4-9e1f-46e9-ad98-157d8668a517

[This dataset is embargoed until August 1, 2025]. The dataset contains monitoring data for plants (surveyed using quadrats), sward insects (surveyed using suction samplers), insect pollinators (surveyed using transects) and sward structure (surveyed using drop disk height) across 60 calcareous grassland restoration sites from southern England and a further six wildcard sites made up of ancient grasslands and rewilding sites in 2021. Information on site management during the sampling year are also included as well as the historic age of the restoration site. Data were collected with the goal of understanding changes in the complexity of plant and invertebrate communities during grasslands restoration with a focus on the effects of duration of restoration. Full details about this dataset can be found at https://doi.org/10.5285/552977d4-b9aa-4932-a055-5ea3bdf16d56

This dataset contains untargeted metabolomic profiles of paired cultures of 4 interacting Streptomyces bacteria. The bacteria were originally isolated from a nature reserve in Minnesota USA. Daily samples of spot cultures grown in adjacent pairs on solid ISP2 medium were taken from day 2 to 6 in triplicate. Metabolites were obtained by cold methanol extraction, and liquid chromatography-mass spectrometry data was collected in both positive and negative ionisation modes. This dataset contains raw LC-MS (liquid chromatography mass spectrometry) data converted to the open-source .mzML format, of each sample run in both positive and negative ionisation modes. This includes, mass-to-charge measurements, elution time, and ion count intensity. These data were collected to investigate how the metabolism of the interacting strains changes in response to neighbouring bacterial cultures. This work was funded by NERC (NE/T010959/1) and National Science Foundation (USA, 1935458). Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/475d6c02-45bb-4ac4-a31a-4a23c7c9cb2e