A diversity of diversities: do complex environmental effects underpin associations between below‐ and above‐ground taxa?

•1. To predict how biodiversity will respond to global change, it is crucial to understand the relative roles of abiotic drivers and biotic interactions in driving associations between the biodiversity of disparate taxa. It is particularly challenging to understand diversity–diversity links across domains and habitats, because data are rarely available for multiple above- and below-ground taxa across multiple sites. •2. Here, we analyse data from a unique biodiversity data set gathered across a variety of oceanic temperate terrestrial habitats in Wales, comprising 300 sites with co-located soil microbial, plant, bird and pollinator surveys along with climate and soil physicochemical information. Soil groups are analysed using metabarcoding of the 16S, ITS1 and 18S DNA regions, allowing in-depth characterisation of microbial and soil animal biodiversity. •3. We explore biodiversity relationships along three aspects of community composition: First, we assess correlation between the alpha diversity of different groups. Second, we assess whether biotic turnover between sites is correlated across different groups. Finally, we investigate the co-occurrence of individual taxa across sites. In each analysis, we assess the contribution of linear or nonlinear environmental effects. •4. We find that a positive correlation between alpha diversity of plants, soil bacteria, soil fungi, soil heterotrophic protists, bees and butterflies is in fact driven by complex nonlinear responses to abiotic drivers. In contrast, environmental variation did not account for positive associations between the diversity of plants and both birds and AM fungi, suggesting a role for biotic interactions. •5. Both the diversity and taxon-level associations between the differing soil groups remained even after accounting for nonlinear environmental gradients. Above-ground, spatial factors played larger roles in driving biotic communities, while linear environmental gradients were sufficient to explain many group- and taxon-level relationships. •6. Synthesis. Our results show how nonlinear responses to environmental gradients drive many of the relationships between plant biodiversity and the biodiversity of above- and below-ground biological communities. Our work shows how different aspects of biodiversity might respond nonlinearly to changing environments and identifies cases where management-induced changes in one community could either influence other taxa or lead to loss of apparent biological associations

Divergent national-scale trends of microbial and animal biodiversity revealed across diverse temperate soil ecosystems

Soil biota accounts for ~25% of global biodiversity and is vital to nutrient cycling and primary production. There is growing momentum to study total belowground biodiversity across large ecological scales to understand how habitat and soil properties shape belowground communities. Microbial and animal components of belowground communities follow divergent responses to soil properties and land use intensification; however, it is unclear whether this extends across heterogeneous ecosystems. Here, a national-scale metabarcoding analysis of 436 locations across 7 different temperate ecosystems shows that belowground animal and microbial (bacteria, archaea, fungi, and protists) richness follow divergent trends, whereas β-diversity does not. Animal richness is governed by intensive land use and unaffected by soil properties, while microbial richness was driven by environmental properties across land uses. Our findings demonstrate that established divergent patterns of belowground microbial and animal diversity are consistent across heterogeneous land uses and are detectable using a standardised metabarcoding approach

Integrated ecological monitoring in Wales: the Glastir Monitoring and Evaluation Programme field survey

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