This dataset contains data from the National Plant Monitoring Scheme between 2015 and 2016. These consist of plant species occurrences, with abundance values, in plots. Plots are nested with 1 km squares, and are georeferenced according to the British/Irish/Channel Islands grid systems, or in latitude/longitude format; the 1 km squares surveyed were selected according to a weighted-random design, designed to enrich the sample for semi-natural habitats. Plots also have associated habitat and spatial information, as well as a small number of other environmental data. The species recorded in any particular plot are dependent on the habitat chosen for the plot by the surveyor, and the level of the scheme at which they were participating. Please see the references in the supporting documentation (survey guidance) for more information. Full details about this dataset can be found at https://doi.org/10.5285/ba39167d-421a-4bfb-b504-56e9f8a38746

This dataset contains data from the National Plant Monitoring Scheme in 2015. These consist of plant species occurrences, with abundance values, in plots. Plots are nested with 1 km squares, and are georeferenced according to the British/Irish/Channel Islands grid systems, or in latitude/longitude format; the 1 km squares surveyed were selected according to a weighted-random design, designed to enrich the sample for semi-natural habitats. Plots also have associated habitat and spatial information, as well as a small number of other environmental data. The species recorded in any particular plot are dependent on the habitat chosen for the plot by the surveyor, and the level of the scheme at which they were participating. Please see the references in the supporting documentation (survey guidance) for more information. Full details about this dataset can be found at https://doi.org/10.5285/33fe87f9-d45a-41ba-acca-ee8585ea6b7d

This dataset gives axiophyte score for plants in Great Britain, based on Botanical Society of Britain and Ireland (BSBI) published lists of axiophytes for 24 counties in Great Britain. Axiophytes have been defined as 'worthy plants', that is, species that are indicative of high quality habitat within a particular region. This information product takes the county lists that were available in May 2016, and summarises the data in order to produce national (i.e. Great Britain) level scores of 'axiophyte-ness'; that is, the extent to which a species has been selected as a good indicator of high quality habitat where it occurs. This meta-list of axiophytes will be updated in the future as more county-level lists become available. 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/af2ac4af-12c6-4152-8ed7-e886ed19622b

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

Data comprise monitoring records of a population of Gryllus campestris, a flightless, univoltine field cricket that lives in and around burrows excavated among the grass in a meadow in Asturias (North Spain). The area has an altitude range from around 60 to 270 metres above sea level. The data include birth and death days, age at capture, air temperature and calling activity. Data were collected from 2006 to 2016. Full details about this dataset can be found at https://doi.org/10.5285/5c8c8f74-5287-4251-87f7-2b965b400624

This data resource provides plot-level plant occurrence data for the first three years (2015-2017) of the National Plant Monitoring Scheme (covering the UK, the Channel Islands, and the Isle of Man). Data consist of individual observations of plants, and other habitat characteristics, at the metre-scale; observations are accompanied by percentage cover information recorded according to the Domin frequency-abundance scale commonly used in plant community ecology. Other information provided includes the plot type (size, shape, according to the NPMS classification), the volunteer-recorded NPMS habitat, the date of sampling, and information regarding the spatial location of the plot. Information contained within the metadata file should allow users to reconstruct the sampling history (including gaps) of any plot that has been sampled within the NPMS scheme between 2015 and 2017. Full details about this dataset can be found at https://doi.org/10.5285/890e4d3d-ae93-4528-8e88-bc01bae3907b

[THIS APPLICATION HAS BEEN WITHDRAWN]. MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 2.10.1 and depends on R packages 'leaps', 'earth', 'fields' and 'mgcv'. Full details about this application can be found at https://doi.org/10.5285/c4d0393e-ff0a-47da-84e0-09ca9182e6cb

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

Field-pathological findings of 33 saiga antelope carcasses (adults and new-born) found in two sites (Tengiz and Turgai, Kazakhstan) during a mass die-off event in May 2015. In Kazakhstan May 2015, approximately 200,000 saiga antelopes died within a month-period causing a loss of two-thirds of the global population. The dramatic event occurred during calving season when females and young males stop migrating and form massive aggregations for calving purposes. With 100% morbidity and 100% mortality of affected herds observed, the 2015 die-off left the largest saiga population, Betpak-Dala, with approximately 30,000 survivors based on post mortality census, highlighting the imminent extinction threats to this critically endangered species. The lack of pathological investigations during historical mass mortality events has limited our understanding of disease-related mortalities in saiga antelope. Generally, aetiological agents were isolated from dead saiga, but the disease course and a full necropsy were not performed nor present in the records. However, for the first time, a full pathology report was possible during 2015. Full details about this dataset can be found at https://doi.org/10.5285/30cbfba7-f9a1-47d5-abdb-f2741041e487

This data resource provides plot-level plant occurrence data for the first five years (2015-2019) of the National Plant Monitoring Scheme (covering the UK, the Channel Islands, and the Isle of Man). Data consist of individual observations of plants, and other habitat characteristics, at the metre-scale; observations are accompanied by percentage cover information recorded according to the Domin frequency-abundance scale commonly used in plant community ecology. Other information provided includes the plot type (size, shape, according to the NPMS classification), the volunteer-recorded NPMS habitat, the date of sampling, and information regarding the spatial location of the plot. Information contained within the metadata file should allow users to reconstruct the sampling history (including gaps) of any plot that has been sampled within the NPMS scheme between 2015 and 2019. 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/cdb8707c-eed7-4da7-8fa3-299c65124ef2