This dataset includes stream and rainfall hydrochemistry of the Plynlimon (Pumlumon in Welsh) research catchments in Mid Wales. The data cover the period from March 2024 to March 2025. Sampling was carried out every four weeks by a team of researchers from UK Centre for Ecology & Hydrology. Stream samples were taken using a grab technique and filtered in the field. Rain samples were collected using bulk precipitation collectors. Data are presented for major anions and cations, pH, conductivity, alkalinity and in-situ measurements for the water temperature and stream flow for the six stream locations and air temperature and volume for the rainfall sites. Full details about this dataset can be found at https://doi.org/10.5285/bb431417-01e8-43e7-bfe5-e48441b2e091

This dataset presents daily data from temperature and soil moisture sensors in each experimental plot (n=9 plots). Soil temperature is measured at 5 cm and 20 cm soil depth (degrees Celsius), and soil moisture is measured as soil volumetric water content (m3 per m3). Data were collected from the climate change field site Climoor that is located in Clocaenog forest, NE Wales. The experimental field site consists of three untreated control plots (Plots 3, 6 and 9), three plots where the plant canopy air is artificially warmed during night time hours (Plots 1, 2 and 7) and three plots where rainfall is excluded from the plots at least during the plants growing season (Plots 4, 5 and 8). Data is an extension for the micromet datasets 1998-2015, 2015-2016, 2016-2021 and 2022-2023 covering the time period January 2024 to December 2024. Soil temperatures are measured with a T107 sensor from Campbell scientific. Soil moisture is measured with CS616 sensors from Campbell scientific. Temperature and moisture data are logged in minute intervals and are averaged as half-hourly. The Climoor field experiment intents to answer questions regarding the effects of warming and drought on ecosystem processes. The reported plot level temperature and soil moisture data are important to evaluate the effect of the imposed climatic treatments on ecosystem processes and functioning. Data collection, processing and quality checks were carried out by UKCEH staff. More detailed information about the field site, measurements and related datasets can be found in the documentation accompanying the data. Full details about this dataset can be found at https://doi.org/10.5285/21e8957f-48b2-4e29-8aa5-247cf060a59c

[This dataset is embargoed until October 1, 2026]. This dataset reports concentrations of six antibiotics—oxytetracycline, ciprofloxacin, enrofloxacin, sulfamethazine, ofloxacin, and norfloxacin—measured in surface water and wastewater from two catchments in India: Dera Bassi (Punjab) and Bhiwadi (Rajasthan). Samples were collected at four time points between May 2022 and June 2023 from the River Ghaggar and wastewater effluent sites. Full details about this dataset can be found at https://doi.org/10.5285/1a4588b8-7145-49ce-8bd5-0895d385932d

This dataset maps the risks of Phytophthora ramorum infection for 79062 larch fragments identified in Scotland, of which 79060 were assigned a risk score. Two fragments without climate and/or habitat suitability scores were excluded. Fragment risk scores integrate multiple risk factors. The primary risk factor is climate suitability for P. ramorum using pathogen-specific temperature-dependent growth curves and a relative humidity threshold. Other risk factors integrated are proximity to larch/infected larch within 500m or 5000m and other (non-Larch) wider environment infections within 1500m. Presence of alternative hosts are scored using habitat suitability within the fragment for sporulating hosts (Vaccinium myrtillus, V. vitis-idaea, Acrostaphylos uva-versi) and the reservoir host Rhododendron ponticum. Additionally, proximity to inspected premises with confirmed infections and the presence of water courses are also assessed. The maximum possible risk score for a larch fragment is 12. Fragments were also classified as low, medium or high risk. Full details about this dataset can be found at https://doi.org/10.5285/f6809e00-91cb-494d-babd-5d60d938ad97

This dataset holds daily data from one automated weather station (AWS) located at the Climoor field site in Clocaenog forest, North East Wales. The data are on relative humidity (percent), air temperature (degrees Celsius), rainfall (millimetres), air pressure (millibars), net radiation (millivolts), solar radiation (kilowatts per square metre per second), photosynthetic active radiation (PAR), (micromol per square metre per second), wind speed (metres per second) and wind direction (degrees). Data is an extension for the AWS datasets 1999-2015, 2015-2016, 2016-2021, 2022-2023 and covering the time period January 2024 to December 2024. Data are logged in minute intervals, averaged to half-hourly. Data are sent from the field site to a UKCEH server. A working copy is created, quality assurance checks carried out and daily averages calculated from half-hourly records. Data which were not recorded are marked with “NA”, faulty data were replaced by “-9999”. Note, the rainfall sensor was broken during this time period, but the column is kept in the datafile for consistency with previous data records. Data collection, processing and quality checking was carried out by members of CEH and UKCEH staff. The following measures were taken with sensors from Campbell Scientific: Rainfall sums are measured with an ARG100 Tipping bucket, air pressure is measured with a CS100 Barometer. Further, Solar radiation and PAR are measured using a Skye SP1110 pyranometer and a SKP215 quantum sensor from Skye Instruments. Wind direction and speed were recorded using a windsonic 2D Ultrasonic Anemometer from Windsonic. The Climoor field experiment intends to answer questions regarding the effects of warming and drought on ecosystem processes. The reported data are collected to monitor site specific environmental conditions and their development over time. These data are important to interpret results that are collected from the climate change manipulations imposed in the field. Full details about this dataset can be found at https://doi.org/10.5285/6b3b766f-4d04-4a41-b2a4-6ca2dd1ea23a

This dataset includes stream and rainfall hydrochemistry of the Plynlimon (Pumlumon in Welsh) research catchments in Mid Wales. The data cover the period from March 2023 to March 2024. Sampling was carried out every four weeks by a team of researchers from UK Centre for Ecology & Hydrology. Stream samples were taken using a grab technique and filtered in the field. Rain samples were collected using bulk precipitation collectors. Data are presented for major anions and cations, pH, conductivity, alkalinity and in-situ measurements for the water temperature and stream flow for the six stream locations and air temperature and volume for the rainfall sites. Full details about this dataset can be found at https://doi.org/10.5285/bd33173b-025f-4c50-aa15-0b853d4774bc

[This dataset is embargoed until May 1, 2026]. The dataset is in csv format recording trait values measured on a subset of the Bengal and Assam Aus Panel (BAAP) of rice grown in the field at two levels of nitrogen application in four South Asian sites over at least two seasons. Traits are leaf nitrogen content (measured with a Soil Plant Analysis Development (SPAD) meter) at 45 and 60 days, days to flowering and at harvest plant height, number of tillers, straw biomass and grain yield. The purpose was to assess members of the BAAP for their response to nitrogen treatment in the field for identification of rice cultivars for high nitrogen use efficiency (NUE) plus quantitative trait loci (QTL) and candidate genes for NUE Full details about this dataset can be found at https://doi.org/10.5285/1e20a1c8-6aeb-4365-866d-71b24c497586

This dataset scores the relative risks of Phytophthora x alni infection for 50034 fragments in Scotland identified as containing any of the three alder species susceptible to the pathogen (common alder, Italian alder and grey alder). Fragment risk scores integrate climate suitability (using pathogen-specific temperature-dependent growth curves and a relative humidity threshold), proximity to rivers/flooding, connectivity to other alder fragments via flood events and recent planting of alder under forestry grant schemes. Data and models used to score risk factors are variable in their time frames, but are broadly representative of the period from 2013 to 2023. Phytophthora disease of alder has been widespread in southern England since at least 1995 and became more prevalent in annual surveys between 1994 and 2003. It has been confirmed at several riparian sites in Scotland. P. x alni diseases are reported only from the alder genus. Full details about this dataset can be found at https://doi.org/10.5285/824f9ba8-7d1c-4a82-b5ec-a4f850f1d370

This dataset maps the potential vulnerability to Phytophthora pinifolia infection for 82 core fragments of the Caledonian Pinewood Inventory (CPI) in Scotland. Fragment risk scores integrate climate suitability for pathogen growth, proximity to other pine fragments and inspected premises and recent planting under forestry grant schemes. Data and models used to assess risks are variable in their time frames, but are broadly representative of the period from 2013 to 2023. The risk maps for Caledonian Pine assess the vulnerability of CPI fragments, if P. pinifolia were to arrive, and the most probable areas of introduction and establishment. P. pinifolia has not been detected in the UK but is on the UK Plant Health Risk Register. All known host species are within the genus Pinus, either recorded as naturally occurring disease affecting the shoots and needles of Pinus radiata, or established through pathogenicity trials on other species within the genus, albeit with varying levels of susceptibility. The only known source region for the pathogen is Chile. P. pinifolia has been ranked 8th among Phytophthora species mostly likely to arrive among the 109 Phytophthora species assessed, with this higher risk predominantly driven by the model component describing climatic similarity between forest, agricultural and urban habitats in Chile and the UK. A Rapid Pest Risk Analysis for P. pinifolia considered it unlikely to enter the UK due to restrictions on import of living pine species from non-EU countries, though transport may still be possible with other, unknown, hosts. Full details about this dataset can be found at https://doi.org/10.5285/ddee75ae-2ad0-4d16-81a9-20928d89e872

This dataset was constructed to understand the perceptions of respondents about pine tree invasion in three communes in central-southern Chile: Santa Juana, Constitución and Tucapel. In addition, the factors that influence the perception of the species and the interest of each community to participate in community control strategies were identified. Face-to-face interviews were conducted in two communities affected by megafires (Santa Juana and Constitución) and one community not affected by such an event (Tucapel), in order to check if there are differences in the willingness of the respondents. The variables evaluated include: (a) demographic data; with information on location, gender, education, age, economic activities and sectors of the respondents; (b) beliefs; whether they think that alien species damage the ecosystem; benefit people; and whether they think that the pine tree harms the traditions of the community; (c) what uses they give to wild pine trees; as fuel, construction material, economic, recreational and cultural purposes; (d) relationship between pine trees and forest fires; if they think that wild pine favours intense and frequent fires, if all vegetation has the same fire risk, and if they think that pine trees can grow back easily after fires, and (e) responsibilities associated with management; if they have ever controlled wild pine in their sector, personal, community, business and government responsibilities associated with management, and how likely they are to participate in strategies to control wild pine. Data were collected between November 2023 and January 2024. Full details about this dataset can be found at https://doi.org/10.5285/63e72aa5-6ea3-4e9f-93fa-311605d3d290