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 2014), 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 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 (2015) is now available. Full details about this dataset can be found at https://doi.org/10.5285/93c14e8e-0353-469b-8adc-420c227ac8f4

A data set consisting of seventeen functional traits collected on 43 saplings from a Control and 33 saplings from a long-term drought experiment site in a tropical rainforest in NE Amazonia, Brazil. The experiment was designed to exclude 50% of the incoming rainfall to the soil and was conducted over a 1ha area, alongside the experiment there is a control (non- drought plot) of a corresponding size. The samples were collected in 2017, fifteen years after the start of the experiment on trees with a diameter at breast height (1.3m) of 1-10cm. The purpose of the dataset was to assess if traits relating to plant metabolism (photosynthesis and respiration) and plant hydraulic processes had been significantly altered in trees growing under drought conditions. Full details about this dataset can be found at https://doi.org/10.5285/ca147ac9-ac68-4348-b5f0-dcd483ef3a85

The data comprise high resolution climate measurements including temperature, precipitation, relative humidity, wind gust speed, wind direction and dew point measured in an open space in proximity to a forest fragment close to Sirsi, Western Ghats, India in 2021 and 2022. The data provide background information used to determine the safety margins of Indian tropical forests to elevated temperatures and dry conditions. Please see related data for information on leaf temperature, soil moisture, and growth, hydraulic, thermal and photosynthetic traits for a representative set of plant species. Full details about this dataset can be found at https://doi.org/10.5285/d7b0dc22-053d-4a91-8e90-2f8b2ab794e4

This dataset includes time-series of pre-dawn and mid-day leaf water potentials for 10 tree species. The time-series covers one year with leaf water potentials measured every three months. These data when combined with hydraulic conductance vulnerability curves (also measured during this project) are an indicator of a tree's safe operating space under dry and high vapour pressure deficit conditions. The overall purpose of the measurements made by this project is to determine eco-physiological limits of functioning of tropical trees in Western Ghats India and to relate them to continuously measured temperature and water status of tropical forest trees at a representative forest site (Sirsi). Full details about this dataset can be found at https://doi.org/10.5285/252b6a14-8a0e-4a6f-a879-99dff46fec71

This dataset contains modelled ammonia recapture by shelterbelts downwind of an ammonia source. The runs were located in the UK with ten years of averaged meteorological data for the different seasons of the year. Several tree species and tree heights were also taken into consideration and predictions of recapture were made for the year 5, 15, 25 and 50 after planting . Two models were coupled to obtain this dataset; Open-source Field Operation And Manipulation (OpenFOAM) and MOdel of Dispersion and Deposition of Ammonia over the Short-range in two dimensions (MODDAS-2D). This dataset was created by the Modelling Uncertainty for Decision Making on Ammonia mitigation with Trees in the landscape (MUDMAT) project funded by NERC. Full details about this dataset can be found at https://doi.org/10.5285/83a2a25c-dbb7-4760-ae3d-21904f3dd278

This dataset contains information on the occupancy of breeding sites in the non-breeding season, breeding timing and success, and breeding site quality for a sample of common guillemots breeding on the Isle of May, Scotland. Data is available for all attributes for the non-breeding season of 2017/18, 2018/19 and 2019/20. These data are part of the Isle of May long-term study to assess population trends of seabirds under environmental change (IMLOTS). Full details about this dataset can be found at https://doi.org/10.5285/40d28d0b-f93d-4c6c-90ef-97a26a510f81

The dataset contains information of Diameter at Breast Height (DBH) of 8,729 trees. These trees are distributed in 29 RAINFOR network forest plots across the Brazilian Amazon, comprising the states of Acre, Mato Grosso and Pará. All the plot censuses are located in terra-firme non-flooded lowland forests. The measurements were collected between 2017 and 2019. The Amazon Forest Inventory Network is a long-term, international collaboration to understand the dynamics of Amazon ecosystems. Since 2000 they have developed a framework for systematic monitoring of forests from the ground-up, centred on plots that track the fate of trees and species, and includes soil and plant biogeochemical records, as well as intensive monitoring of carbon cycle processes at some sites. RAINFOR works with partners across the nations of Amazonia to support and sustain forest monitoring and help develop new generations of Amazon ecologists. The work of RAINFOR is currently supported by funding agencies in Brazil, the UK, and the EU. Full details about this dataset can be found at https://doi.org/10.5285/63d4b774-4e03-4db2-95ad-dcca18f0d681

This dataset derives from cross-over experiments using ant worker rescue behaviour towards caterpillars of the socially parasitic butterfly from two host-ecotypes. The data comprise datasets collected from four 4 experiments 3 hours after testing and from 4 experiments 7 days later. They all include nest numbers, the order of retrieval ranked by the attention of nurse ants to the ant pupae, large larvae and small larvae and the adult Maculinea rebeli. The data give the rank order of test items as they were rescued in order to explain social status achieved in natural and unnatural host colonies. This dataset is part of the study of mimetic host shifts in an endangered social parasite of ants, which is a joint study of the NERC's Centre for Ecology & Hydrology (UK), the University of Oxford (UK), University of Bialystok (Poland), Polish Academy of Sciences (Poland) and UFZ Helmholtz Centre for Environmental Research (Germany). Detailed research method can be found in Thomas et al. (2012) Mimetic host shifts in an endangered social parasite of ants. Proc. R. Soc. B vol. 280 no.1751. (http://doi.org/10.1098/rspb.2012.2336) Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/d6a8bc3d-b6fb-47bc-a693-612e2454cf50

This dataset models 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). Full details about this dataset can be found at https://doi.org/10.5285/cc5ae9b1-43a0-475e-9157-a9b7fccb24e7

Bulk elemental (carbon and nitrogen) and stable isotope (delta 13C and delta 15N) data produced from 491 samples collected between 2016-2021 from terrestrial (soil, peat, living biomass, dead biomass), intertidal (saltmarsh vegetation, saltmarsh roots, seagrass biomass, mudflat, faecal matter) and marine (macroalgae, microalgae zooplankton, finfish aquaculture waste) environments across the UK. These samples alongside analytical standard derived from natural materials (lignin, humic acid, cellulose, glucose, protein) were analysed to determine their bulk elemental (organic carbon and nitrogen) and stable isotope (delta 13C org and delta 15N) composition. These values are envisioned to be used to constrain organic carbon sources (terrestrial vs marine) in the natural environment when used alongside isotope mixing models. The work was carried out under the NERC programme - Carbon Storage in Intertidal Environment (C-SIDE), NERC grant reference NE/R010846/1. Full details about this dataset can be found at https://doi.org/10.5285/a445a7a8-528d-4e0b-9094-28cbcd449367