This data resource provides plot-level plant occurrence data for the first seven years (2015-2021) 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 2021. 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/e742c94f-82a4-43e7-af14-36b131afe81b

This dataset contains the transcripts of interviews on the current structure of the main charcoal supply chains from seven villages in the Mabalane district of Gaza province to Maputo, Mozambique. The seven villages were at different stages of the charcoal production process within similar soils, rainfall, and vegetation types. Semi-structured interviews and focus group discussions were conducted with key informants (e.g. village leaders, charcoal producers, wholesalers, truck drivers and forest officers), from May to October 2014. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/56bae7ce-4798-41a4-b702-2f847952f7ae

This data resource provides plot-level plant occurrence data for the first eight years (2015-2022) 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 2022. 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/f7ef2dc5-2bce-4436-8f65-90f7a99acff2

This dataset contains the transcripts of interviews and discussion groups from seven villages in the Mabalane district, Gaza province, Mozambique. The seven villages were selected from a forest degradation gradient running from villages with abundant undisturbed forest areas to those with degraded forests, mainly driven by charcoal production. The villages had similar infrastructure, soils, rainfall, and vegetation types. The dataset contains information on seasonality, how availability and use of products from the forest has changed over time (trend analysis), wealth ranking within the villages and differences between wealth statuses, and profiles/characteristics of each village. Interviews were conducted with groups in each village or the leader of the village, between May and September 2014. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/49a70237-c579-4669-b126-3f23d494aba6

Data comprise scenarios of how land use can be in the future and how will it affect ecosystem services in rural Mozambique. The scenarios were constructed from information gathered at five workshops held in Maputo, Xai Xai, Quelimane and Lichinga in 2014 and 2015. The objective of the workshops was to examine aspects that influence well-being (e.g. ecosystem services) and their causes (e.g. change in land use) in the Miombo woodland area of rural Mozambique and identify actions that could contribute to poverty alleviation and biodiversity conservation. The final objective was to construct scenarios of how the land use can be in Mozambique in the future (2035). The data were collected as part of the Abrupt Changes in Ecosystem Services and Wellbeing in Mozambican Woodlands (ACES) project and were funded by the Ecosystem Services for Poverty Alleviation (ESPA) programme, funded by NERC, the Economic & Social Research Council (ESRC) and the Department for International Development (DfID), the three are government organizations from UK. The project was led by the University of Edinburgh, with the collaboration of the Universidad Mondlane, the IIED, and other organizations. Full details about this dataset can be found at https://doi.org/10.5285/97c65c35-1db5-49d5-8ee0-ae5c7b699634

This dataset contains the transcripts of interviews and discussion groups from ten villages in the Gurue district, Zambezia province, Mozambique. The ten villages were selected from a land scarcity gradient running from villages with abundant land to those with intense land constraints, mainly driven by expanding agricultural activities and population density. The villages had similar infrastructure, soils, rainfall, and vegetation types. The dataset contains information on participatory mapping of the village characteristics, seasonality, how agricultural activity has changed over time (trend analysis), wealth ranking within the villages and differences between wealth statuses, and profiles/characteristics of each village. Interviews were conducted with groups in each village or the leader of the village, between July and December 2015. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/f82f7ad8-0e98-41cb-951e-be64ffd36078

This dataset contains the transcripts of interviews and discussion groups on the current soya production from ten villages in the Gurue district, Zambezia province, Mozambique. The ten villages were selected from different stages of a land scarcity gradient running from villages with abundant land to those with intense land constraints, mainly driven by expanding agricultural activities and population density. The villages had similar infrastructure, soils, rainfall, and vegetation types. Interviews and focus group discussions were conducted with village small-scale soya producers, emergent soya farmers, managers of commercial soya producing operations, district officer, technicians of NGOs, formal and informal traders between July and December 2015. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/18c8cbf3-ed55-4065-b52b-b2a4c2ffdf1c

Data comprise causal diagrams which show links between aspects that influence the well-being of rural inhabitants (e.g. good quality of food, good family relationships, education, etc) with ecosystem services (e.g. food from trees, wood sticks for construction, firewood, wood for charcoal production, etc.) and their causes (e.g. change in land use) in rural Mozambique. Information was gathered at 20 workshops held in Maputo, Xai Xai, Lichinga, Quelimane, and at village level in the districts of Mabalane, Marrupa and Gurue in 2014 and 2015. The objective of the workshops was to examine aspects that influence well-being and their causes in the miombo woodland area of rural Mozambique. One of the objectives of the project was to construct Bayesian belief networks (BBNs) to model future land use change scenarios in rural Mozambique using a participatory approach, to evaluate the consequences of deforestation in the well-being of the rural population. The data were collected as part of the Abrupt Changes in Ecosystem Services and Wellbeing in Mozambican Woodlands (ACES) project and were funded by the Ecosystem Services for Poverty Alleviation (ESPA) programme, funded by NERC, the Economic & Social Research Council (ESRC) and the Department for International Development (DfID), the three are government organizations from UK. The project was led by the University of Edinburgh, with the collaboration of the Universidad Mondlane, the IIED, and other organizations. Full details about this dataset can be found at https://doi.org/10.5285/14622c4b-8bd4-4624-8ea6-35da7da211cd

These data consist of information on economic, social, demographic, cultural, and treatment seeking behaviour collected from former and current human African trypanosomiasis (HAT) patients in Eastern Zambia between 2004 and 2014. There are two data sets. The first dataset consists information on the economic and social impact of HAT. Information on demographics, culture, and treatment seeking behaviour was also collected. Data for this dataset were collected through structured questionnaires administered to patients themselves or their close relatives (care giver). The questionnaires were developed and delivered by experienced researchers from the University of Zambia. The data have been anonymised by removing the names of villages where the patients lived. In total, 64 cases were included in the study. Verbal consent was obtained prior to commencing all questionnaires. The second dataset consists of anonymised transcripts of focus group discussions conducted with health workers, people who have suffered from HAT and their relatives or friends. Seven to ten people were included per discussion group, providing information on concepts, perceptions and ideas relating to the social consequences of HAT. A total of eight focus group discussions were conducted during the study. Focus group discussion data were analysed using inductive approaches and thematic coding carried out by two independent researchers. All transcripts were anonymised and personal identifiers were removed to protect patients' individual data. Verbal consent was obtained prior to commencing all interviews. Focus group interviews were carried out by experienced researchers from the University of Zambia. The data were collected to determine the economic and social consequences of human African trypanosomiasis (HAT) in Eastern Zambia. This research was part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC), and these data contributed to the research carried out by the consortium. The research was funded by NERC project no NE/J000701/1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Full details about this dataset can be found at https://doi.org/10.5285/6f70d562-8fcf-4ecd-adaf-cbc5800cc326

This data set contains Global maps of five ecosystem services using 6 different among-model ensemble approaches: the provisioning services of water supply, biomass for fuelwood and forage production, the regulating service Carbon Storage for CO2 retention and the cultural non-material service Recreation. For water, the data comes as one shapefile with polygons per watershed, each polygon containing seven ensemble estimates. The other services – recreation, carbon storage, biomass for fuelwood and forage production – come as seven tiff- maps at a 1-km2 resolution with associated world files for each tiff-map contains 43,200 x 18,600 pixels for one ensemble approach, with LZW compressed file sizes between 400MB and 950MB. For all maps, 600dpi jpg depictions are added to the supporting information with uniform colour scaling set for the median ensemble per service. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme, with additional funding from ES/R009279/1 (MobilES) & ES/T007877/1 (RUST). Full details about this dataset can be found at https://doi.org/10.5285/bd940dad-9bf4-40d9-891b-161f3dfe8e86