The data consists of total arbuscular mycorrhizal fungi colonisation in fine roots in old growth forests in Central Amazon. Fine roots younger than three months were sampled using the ingrowth core technique in a large-scale nutrient fertilisation experiment. Total mycorrhizal colonisation is given as a mean of the plant community per plot, where five points inside each plot were sampled in the 0-10 cm soil layer. The spreadsheet depicts the percentage of fine root length colonised by different arbuscular mycorrhizal structures (hyphae, arbuscules and vesicles). Samples were collected in February 2018, eight months after the nutrient fertilisation started 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/10.5285/52f756a1-a0f6-4991-b21b-4042018a451f

This dataset consists of the 1km raster, dominant target class version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The 1km dominant coverage product is based on the 1km percentage product and reports the habitat class with the highest percentage cover for each 1km pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. 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 UKCEH 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/d33593d7-5c4d-419e-924c-b341847fd6ae

The data consists of potential phosphatase activity released by fine roots in old growth forests in Central Amazon. Fine roots younger than three months were sampled using the ingrowth core technique in a large-scale nutrient fertilisation experiment. Phosphatase activity is given as a mean of the plant community per plot, where five points inside each plot were sampled and separated in two different soil layers (0-10 and 10-30cm). Samples were collected in February 2018, eight months after the nutrient fertilisation started 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/10.5285/6529004d-b6e8-429d-ac68-0f5324d7e5d0

This dataset consists of the 1km raster, percentage aggregate class version of the Land Cover Map 1990 (LCM1990) for Great Britain. 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/c7195a20-7943-4d5d-9f6e-c9fead472dde

Data comprise concentrations of second-generation anticoagulant rodenticides (SGARs) in Eurasian sparrowhawk (Accipiter nisus) livers found dead in Great Britain between 1995 and 2015. The liver SGARs measured include Bromadiolone, Difenacoum, Brodifacoum, Difethialone, Flocoumafen and sum of SGARs in nanograms per gram-wet weight. The data also include demographic information (Age and Sex) for each bird and location where the bird was found. Members of the public and other interested parties submitted Sparrowhawks to the Predatory Bird Monitoring Scheme (PBMS) after being found dead. The collection, examination, and archiving of birds and their tissues was conducted by the PBMS and supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Contaminants and data analysis was conducted under the CHEMPOP project, NERC grant NE/S000100/1. The UKAS Accredited UK Centre carried out liver analysis for Ecology & Hydrology Lancaster Analytical Chemistry Facility. Full details about this dataset can be found at https://doi.org/10.5285/1af003b1-2f70-4e45-a31a-b07a5fe6e929

The dataset contains measurements of temperature (°C) and light availability (Lux) in rivers in the Hampshire Avon catchment (UK). Six rivers within sub-catchments of contrasting geology (clay, sand, chalk) were investigated. The stream sites monitored were chosen to reflect a gradient of base flow index. Data were obtained via direct, field-based measurements every 15 minutes from February 2013 to (max) December 2014 with sensors tethered to the bed of the river at each site. Full details about this dataset can be found at https://doi.org/10.5285/9b6a6233-85ad-44f4-ba83-4905b8c48713

This 1 km summary pixel data set represents the land surface of Great Britain and Northern Ireland, classified using two classification schemas, target and aggregate classes. The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class. The 1km percentage product provides the percentage cover for each of the 21 land cover classes for 1km x 1km pixels. This product contains one band per habitat class, producing 21 and 10 band images for the target and aggregate class products respectively. The 1km dominant coverage product is based on the 1km percentage product, and reports the land cover class with the highest percentage cover for each 1km pixel. A full description of this and all UKCEH LCM2019 products are available from the LCM2017-19 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/e5632f1b-040c-4c39-8721-4834ada6046a

The dataset comprises the field soil moisture content expressed as a percentage at three depth zones, (0 - 10 centimetre (cm), 10 - 20 cm and 20 - 30 cm), measured from bulk density soil samples taken within each 1 metre x 1 metre quadrat. Sampling was conducted at six salt marsh sites at four spatial scales: 1 metre (m) (the minimal sampling unit) nested within a hierarchy of increasing scales of 1-10 m, 10-100 m and 100-1000 m. Three of the sites were in Morecambe Bay, North West England and three of the sites were in Essex, South East England. The Morecambe Bay samples were taken during the winter and summer of 2013. The Essex samples were taken during the winter, early spring and summer of 2013. Samples were taken using a bulk density ring. Soil moisture content was determined by weighing the samples as fresh field mass and then again after being dried out at 105 degrees Celsius for 72 hours. This data was collected as part of Coastal Biodiversity and Ecosystem Service Sustainability (CBESS): NE/J015644/1. The project was funded with support from the Biodiversity and Ecosystem Service Sustainability (BESS) programme. BESS is a six-year programme (2011-2017) funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) as part of the UK's Living with Environmental Change (LWEC) programme. Full details about this dataset can be found at https://doi.org/10.5285/125a899b-8f10-4803-a6cf-a2fb1739746f

[This dataset is embargoed until May 1, 2025]. The dataset contains information on six functional traits of woody plants, including Leaf Area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Thickness (Lth), Wood Density (WD), Bark Thickness (BT). It also includes data on concentrations of C, N, P, Ca, Mg, and K in leaves; leaf fresh mass and leaf dry mass data; and fresh wood volume and dry wood mass data to calculate Wood Density. Ten leaves per individual were sampled, and three measurements were taken for leaf thickness at the base, middle, and top of the leaf. For wood density, a single branch sample was taken per individual and for bark thickness five measurements per individual were made. The data were collected between 2019 and 2022 from 27 forest monitoring plots (0.5 ha each) in five locations along an altitudinal (lowland, mid-elevation, and highland forests) and forest perturbation (low, medium, and high perturbation levels) gradient in Andean ecosystems in Colombia. The database includes information about the plot location and parameters of the locality. The purpose of this data collection was to determine whether the expression of functional traits in woody plants differs between the perturbation gradient and the relationship of the traits to ecosystem processes. This information is important for understanding the drivers of variation in forest resilience and the impacts of perturbation on ecosystem functioning. This data set was obtained within the framework of the BioResilience project, a transdisciplinary investigation that seeks to understand the resilience of forest ecosystems after the post-conflict period in Colombia. Full details about this dataset can be found at https://doi.org/10.5285/0da218a8-2882-4ee7-bb2a-50a51f7f4138

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