Search

The Glastir Monitoring and Evaluation Programme (GMEP) ran from 2013 until 2016 and was probably the most comprehensive programme of ecological study ever undertaken at a national scale in Wales. The programme aimed to (1) set up an evaluation of the environmental effects of the Glastir agri-environment scheme and (2) quantify environmental status and trends across the wider countryside of Wales. The focus was on outcomes for climate change mitigation, biodiversity, soil and water quality, woodland expansion, and cultural landscapes. As such, GMEP included a large field-survey component, collecting data on a range of elements including vegetation, land cover and use, soils, freshwaters, birds, and insect pollinators from up to three-hundred 1 km survey squares throughout Wales. The field survey capitalised upon the UK Centre for Ecology & Hydrology (UKCEH) Countryside Survey of Great Britain, which has provided an extensive set of repeated, standardised ecological measurements since 1978. The design of both GMEP and the UKCEH Countryside Survey involved stratified-random sampling of squares from a 1 km grid, ensuring proportional representation from land classes with distinct climate, geology and physical geography. Data were collected from different land cover types and landscape features by trained professional surveyors, following standardised and published protocols. Thus, GMEP was designed so that surveys could be repeated at regular intervals to monitor the Welsh environment, including the impacts of agri-environment interventions. One such repeat survey is scheduled for 2021 under the Environment and Rural Affairs Monitoring & Modelling Programme (ERAMMP). Data from GMEP have been used to address many applied policy questions, but there is major potential for further analyses. The precise locations of data collection are not publicly available, largely for reasons of landowner confidentiality. However, the wide variety of available datasets can be (1) analysed at coarse spatial resolutions and (2) linked to each other based on square-level and plot-level identifiers, allowing exploration of relationships, trade-offs and synergies. This paper describes the key sets of raw data arising from the field survey at co-located sites (2013 to 2016). Data from each of these survey elements are available with the following digital object identifiers (DOIs): Landscape features (Maskell et al., 2020a–c), https://doi.org/10.5285/82c63533-529e-47b9-8e78-51b27028cc7f , https://doi.org/10.5285/9f8d9cc6-b552-4c8b-af09-e92743cdd3de , https://doi.org/10.5285/f481c6bf-5774-4df8-8776-c4d7bf059d40 ; Vegetation plots (Smart et al., 2020), https://doi.org/10.5285/71d3619c-4439-4c9e-84dc-3ca873d7f5cc ; Topsoil physico-chemical properties (Robinson et al., 2019), https://doi.org/10.5285/0fa51dc6-1537-4ad6-9d06-e476c137ed09 ; Topsoil meso-fauna (Keith et al., 2019), https://doi.org/10.5285/1c5cf317-2f03-4fef-b060-9eccbb4d9c21 ; Topsoil particle size distribution (Lebron et al., 2020), https://doi.org/10.5285/d6c3cc3c-a7b7-48b2-9e61-d07454639656 ; Headwater stream quality metrics (Scarlett et al., 2020a), https://doi.org/10.5285/e305fa80-3d38-4576-beef-f6546fad5d45 ; Pond quality metrics (Scarlett et al., 2020b), https://doi.org/10.5285/687b38d3-2278-41a0-9317-2c7595d6b882 ; Insect pollinator and flower data (Botham et al., 2020), https://doi.org/10.5285/3c8f4e46-bf6c-4ea1-9340-571fede26ee8 ; and Bird counts (Siriwardena et al., 2020), https://doi.org/10.5285/31da0a94-62be-47b3-b76e-4bdef3037360. [ABSTRACT FROM AUTHOR]

Understanding the consequences of different land uses for the soil system is important to make better informed decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped LOESS regressions (BLRs). This nonparametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil organic carbon (SOC). While this straightforward non-parametric approach may be most useful in comparing SOC profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles. [ABSTRACT FROM AUTHOR]