Many countries are concerned by and wish to arrest or reverse what is termed a biodiversity crisis in invertebrates. To understand the issues facing riverine invertebrates in England, a fully integrated dataset where macroinvertebrate monitoring sites were aligned in space and time with physical, geographic, habitat, and chemical factors from 2003 to 2018 (quantitative abundance data being universally available from 2003) was brought together for statistical analysis. Over this period the median abundance either did not change or for some groups actually increased. The aim was to identify what the principal factors were that influenced Ephemeroptera (Mayflies), Plecoptera (Stoneflies), Trichoptera (Caddisflies), Odonata (Dragonflies and Damselflies), Diptera (True Flies), Coleoptera (Beetles), Hemiptera (True Bugs), and Gastropoda (Snails) abundance over this 16-year period. The dataset was examined using an ensemble framework within two modelling approaches: generalised linear mixed-effects models with permutation-based variable importance, as well as non-linear generalised additive mixed models to assess the percentage of deviance explained by each variable. The range of approaches aimed to offer different perspectives on variable importance, providing a more comprehensive understanding of the data and highlighting how model selection can influence ecological data interpretation. For most groups, physical factors, such as altitude, distance from source, slope, bed substrate and flow discharge, were strong predictors of abundance, likely reflecting natural habitat preferences shaped by evolutionary history. Land cover was also influential, with seminatural areas generally supporting higher abundances and urban land cover associated with lower abundances. Some chemical and ecological factors - such as wastewater and nutrient content, were particularly important for Ephemeroptera, Plecoptera, and Trichoptera abundance. For Coleoptera, Hemiptera, Trichoptera, Diptera and Gastropoda, metal levels played a role in their abundance, whilst for Odonata, mean temperature appeared to be important. Diptera appeared to be relatively insensitive to the factors examined. This statistical examination of large monitoring datasets, with no a priori assumptions, is vital in resolving a key challenge in bioassessment: identifying what influences invertebrate abundance when data are sparse. The results can provide policy options to improve ecological conditions, and the approach is transferable to other regions.

High rates of soil organic carbon (SOC) loss from cropland soils are well known, contributing to climate change and compromising soil and ecosystem health. Stabilising and reversing the loss of organic matter from cropland soils is a challenge for all nations to meet the United Nations Sustainable Development Goals. Sustainable land management (SLM) has been promoted as a mechanism of achieving this, but to date, there is no evidence of positive impacts at scale. Here we show the first signs of the reversal of soil carbon loss in cultivated topsoils in Great Britain, following a period of reported SLM uptake, using 40+ years of national soil monitoring from the UKCEH Countryside Survey. Following a prolonged historic decline at rates of -0.16 t ha-1 year-1, there was a significant increase in cropland topsoil SOC stocks (0-15 cm) from 2007 to 2019-22 with an accrual rate of 0.17 t ha-1 year-1, approximately 0.74 MtC year-1 nationally. We discuss reported management shifts in Great Britain in the corresponding period and identify a reduction in conventional tillage and reduced straw removal as potential drivers, but highlight additional evidence gaps worthy of consideration. This increase in topsoil SOC may represent net carbon sequestration or carbon redistribution (geographic or vertical) but nevertheless demonstrates that topsoil properties can be restored at scale and offers hope that a concerted effort by land managers can halt, and potentially reverse, SOC loss from cropland soils.

Abstract Traditional ecological monitoring employs fixed designs, which do not vary over the survey duration. Adaptive sampling, whereby the data already collected informs a sampling design which changes over the course of the study, can provide a more optimal and flexible survey design but is little used in ecology. We aim to provide an introduction to adaptive sampling for ecologists. We review previous literature and highlight examples of both empirical adaptive approaches, such as adaptive cluster sampling, and more novel model‐based adaptive methods. To conceptualise the process of adaptive sampling we identify four key stages: choice of data, definition of a criterion, selection of new sampling occasions and sampling activity. We discuss each stage in turn and focus on the decisions ecologists need to consider in order to successfully implement an adaptive sampling strategy. We include a full walkthrough of an adaptive sampling example with code provided to demonstrate each step. Adaptive sampling has potential advantages to ecologists but so far has had limited uptake. We review key challenges and barriers to uptake and suggest potential ways forward. We hope our paper will both increase awareness of adaptive sampling methods and provide a useful resource for ecologists considering an adaptive survey design.

Abstract Species distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space. Spatio‐temporal models require large amounts of data spread over time and space, and as such are clear candidates to benefit from model‐based integration of different data sources. However, spatio‐temporal models are highly computationally intensive and integrating different data sources can make this approach even more unfeasible to ecologists. Here we demonstrate how the R‐INLA methodology can be used for model‐based data integration for spatio‐temporally explicit modelling of species distribution change. We demonstrate that this method can be applied to both point and areal data with two contrasting case studies, one using the SPDE approach for modelling spatio‐temporal change in the Gatekeeper butterfly (Pyronia tithonus) across Great Britain and the second using a spatio‐temporal areal model to describe change in caddisfly (Trichoptera) populations across the River Thames catchment. We show that in the caddisfly case study integrating together different data sources led to greater understanding of the change in abundance across the River Thames both seasonally and over 5 years of data. However, in the butterfly case study moving to a spatio‐temporal context exacerbated differences between the data sources and resulted in no greater ecological insight into change in the Gatekeeper population. Our work provides a computationally feasible framework for spatio‐temporally explicit integration of data within SDMs and demonstrates both the potential benefits and the challenges in applying this methodology to real ecological data.

Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15 790 soil cores) and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stock (Sc) in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing, semi-natural grasslands, and improved grasslands, and they were lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this variability was similar across all surveys. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the "space-for-time" substitution, and, although the results were not unequivocal, we estimated that the effects are likely to be overestimated by 5 %-33 %, depending upon land use.

Soils are the largest terrestrial store of carbon, storing more carbon than the atmosphere and the biosphere combined. Soil carbon plays a key role in the delivery of a wide range of ecosystem services including climate regulation, food production, water quality and regulation and as such is often used as a proxy for ‘soil health’. International initiatives such as ‘Carbon 4 per mille’ highlight the potential for carbon sequestration in soils as a mechanism for climate mitigation, and the UK’s NetZero target depends on significant land-based carbon sequestration. Therefore, a need exists to quantify present-day soil carbon stocks at both regional and national scales to guide policy decisions and provide a baseline to enable estimates of carbon sequestration potential. To meet this need Digital Soil Maps (DSMs) have gained significant provenance, providing high-resolution maps through spatial extrapolation of observed data to regional, national and global scales. These maps are created by applying data-science methods to observational point data and associated covariates to create a predictive model. The model is used to extrapolate the prediction over the area for which covariate information is available. The predictive models often indicate impressively high levels of accuracy based on test/validation data. However, due to differences in both the range of data, methods and covariates used to drive predictive models, multiple DSMs created for the same areas are unlikely to be identical, which is indicative of the uncertainty associated with these mapped products. Much like with process-based models, there is a need to understand which data-science methodology is most suitable for a given research question and provide clarity on the magnitude of uncertainty associated with predictions. In this study, we quantify uncertainty in DSMs as a result of methodological choice; we apply several approaches (Random forest, Gaussian Process, Generalised Additive Model, Neural Network and Linear Regression) to create multiple predictive models of SOC concentration across the UK. By allowing the models to select from identical input data we provide a fair comparison of each approach through isolating uncertainty in DSMs as a result of methodological choice. In addition to accuracy assessment of each of the generated DSMs, we evaluate the suitability of each of these methods for DSM application. Most crucially, we highlight the need for caution in relation to the assumed levels of accuracy of generated DSMs when considering only standard validation statistics, and the limitations of these approaches when data has bi-modal distribution, a common feature of data that encompasses both mineral and organic soils. Whilst standard statistics evaluating the overall accuracy of the DSMs are highly significant, levels of accuracy across land use classifications vary considerably. Our study highlights the need for increased transparency in communication of uncertainty and limitations of derived map products.

Land use and its management can play a vital role in carbon sequestration, but trade-offs may exist with other objectives including food security and nature recovery. Using an integrated model (the FABLE calculator), four pathways, co-created with colleagues at the Welsh Government, towards achieving climate and biodiversity targets in Wales were explored: status quo, improvements on current trends, land sparing and land sharing. We found that continuing as usual will not be sufficient to meet Wales’s climate and biodiversity targets. In contrast, the land use and agricultural sector became a net carbon sink in both the land sparing and land sharing pathways, through high afforestation targets, peatland restoration, reducing food waste and moving towards a healthier diet. Whilst both pathways released land for biodiversity, the gains were greater in the land sharing pathway, which was also less dependent on optimistic assumptions concerning productivity improvements. The results demonstrate that alternative approaches to achieving nature-positive and carbon–neutral land use and food systems may be possible, but they come with stringent and transformative requirements for policy changes, with an integrated approach necessary to maximise benefits for climate, food and nature.

Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth‐based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This short article reflects on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We seek to unpick the complex two‐way relationship between data and process understanding. By focusing on the interactions, we end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area.

The riverine dissolved organic carbon (DOC) flux is of similar magnitude to the terrestrial sink for atmospheric CO2, but the factors controlling it remain poorly determined and are largely absent from Earth system models (ESMs). Here, we show, for a range of European headwater catchments, that electrolyte solubility theory explains how declining precipitation ionic strength (IS) has increased the dissolution of thermally moderated pools of soluble soil organic matter (OM), while hydrological conditions govern the proportion of this OM entering the aquatic system. Solubility will continue to rise exponentially with declining IS until pollutant ion deposition fully flattens out under clean air policies. Future DOC export will increasingly depend on rates of warming and any directional changes to the intensity and seasonality of precipitation and marine ion deposition. Our findings provide a firm foundation for incorporating the processes dominating change in this component of the global carbon cycle in ESMs.

Cropping decisions affect the nature, timing and intensity of agricultural management strategies. Specific crop rotations are associated with different environmental impacts, which can be beneficial or detrimental. The ability to map, characterise and accurately predict rotations enables targeting of mitigation measures where most needed and forecasting of potential environmental risks. Using six years of the national UKCEH Land Cover® plus: Crops maps (2015-2020), we extracted crop sequences for every agricultural field parcel in Great Britain (GB). Our aims were to first characterise spatial patterns in rotation properties over a national scale based on their length, type and structural diversity values, second, to test an approach to predicting the next crop in a rotation, using transition probability matrices, and third, to test these predictions at a range of spatial scales. Strict cyclical rotations only occupy 16 % of all agricultural land, whereas long-term grassland and complex-rotational agriculture each occupy over 40 %. Our rotation classifications display a variety of distinctive spatial patterns among rotation lengths, types and diversity values. Rotations are mostly 5 years in length, short mixed crops are the most abundant rotation type, and high structural diversity is concentrated in east Scotland. Predictions were most accurate when using the most local spatial approach (spatial scaling), 5-year rotations, and including long-term grassland. The prediction framework we built demonstrates that our crop predictions have an accuracy of 36-89 %, equivalent to classification accuracy of national crop and land cover mapping using earth observation, and we suggest this could be improved with additional contextual data. Our results emphasise that rotation complexity is multi-faceted, yet it can be mapped in different ways and forms the basis for further exploration in and beyond agronomy, ecology, and other disciplines.

1. In order to better quantify spatial and temporal patterns in freshwater biodiversity, and potential underlying drivers of change, we must utilise the increasingly broad range of data available on freshwater ecosystems. Statistical advances in the field of integrated modelling provide new opportunities to further our understanding through the combined and simultaneous analysis of these diverse datasets. 2. We briefly introduce integrated modelling in the context of freshwater biodiversity and outline the key steps involved in its implementation, from data collection to analysis. We highlight both opportunities and challenges for the application of integrated approaches. 3. To illustrate the potential for integrated models to improve our understanding of freshwater biodiversity compared to standard approaches, we combine two datasets collected using different methods to model the distribution of Agabus water beetles in England. The integrated model had greater power to detect covariate effects on Agabus distribution, and reduced parameter uncertainty compared with analysis using only a single dataset. 4. We show that integrated methods have the potential to increase our understanding of freshwater systems and enable us to make full use of the diversity of freshwater data available.

Soil organic carbon (SOC) concentration is the fundamental indicator of soil health, underpinning food production and climate change mitigation. SOC storage is highly sensitive to several dynamic environmental drivers, with approximately one third of soils degraded and losing carbon worldwide. Digital soil mapping illuminates where hotspots of SOC storage occur and where losses to the atmosphere are most likely. Yet, attempts to map SOC often disagree. Here we compare national scale SOC concentration map products to reveal agreement of data in mineral soils, with progressively poorer agreement in organo-mineral and organic soils. Divergences in map predictions from each other and survey data widen in the high SOC content land types we stratified. Given the disparities are highest in carbon rich soils, efforts are required to reduce these uncertainties to increase confidence in mapping SOC storage and predicting where change may be important at national to global scales. Our map comparison results could be used to identify SOC risk where concentrations are high and should be conserved, and where uncertainty is high and further monitoring should be targeted. Reducing inter-map uncertainty will rely on addressing statistical limitations and including covariates that capture convergence of physical factors that produce high SOC contents.

Due to rising demand for both food and environmental services, agriculture is increasingly required to deliver multiple outcomes. Characterising differences, across agricultural landscapes, via the identification of broad archetypal groupings, is an important step in exploring spatial patterns in the capacity of land to deliver these potentially competing functions. Creating characterisations at multiple levels, for landscape and farm management, can allow policy-makers and land managers to harmonise delivery of ecosystem services at different intervention scales. This can identify ways to increase the complementarity of public goods and the sustainability of farmed landscapes. We used data-driven machine learning to create landscape and agricultural management archetypes (1 km resolution) at three levels, defined by opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. By using a non-nested hierarchy, we identified which types of management are restricted to certain landscape settings, and which are applicable across multiple landscape contexts. Understanding variation within and between agricultural landscapes and farming practices has implications for planning environmental sustainability and food security. It can also aid understanding of the scale at which interventions could be most effective, from incentivising changes in farmer behaviour to policy drivers of large-scale land use change.

Mappings play an important role in environmental science applications by allowing practitioners to monitor changes at national and global scales. Over the last decade, it has become increasingly popular to use satellite imagery data and machine learning techniques (MLTs) to construct such maps. Given the black-box nature of many of these MLTs though, quantifying uncertainty in these maps often relies on sampling reference data under stricter conditions. However, practical constraints can sampling such data expensive, which forces stakeholders to make a trade-off between the degree of uncertainty in predictions and the costs of collecting appropriately sampled reference data. Furthermore, quantifying any trade-off is often difficult, as it will depend on many interdependent factors that cannot be fully understood until more data is collected. This paper investigates how a combination of Bayesian inference and an adaptive approach to sampling reference data can offer a generalizable way of managing such trade-offs. The approach is illustrated and evaluated using a woodland mapping of England as a case study in which reference data is collected under constraints motivated by COVID-19 travel restrictions. The key findings of this paper are as follows: (a) an adaptive approach to sampling reference data allows an informed approach when quantifying this trade-off; and (b) Bayesian inference is naturally suited to adaptive sampling and can make use of Monte Carlo methods when dealing with more advanced problems and analytical techniques.

Long-term change and shorter-term variability in the atmospheric deposition of pollutants and marine salts can have major effects on the biogeochemistry and ecology of soils and surface water ecosystems. In the 1980s, at the time of peak acid deposition in the UK, deposition loads were highly dependent on prevailing weather types, and it was postulated that future pollution recovery trajectories would be partly dependent on any climate change-driven shifts in weather systems. Following three decades of substantial acidic emission reductions, we used monitoring data collected between 1992 and 2015 from four UK Environmental Change Network (ECN) sites in contrasting parts of Great Britain to examine the trends in precipitation chemistry in relation to prevailing weather conditions. Weather systems were classified on the basis of Lamb weather type (LWT) groupings, while emissions inventories and clustering of air mass trajectories were used to interpret the observed patterns. Concentrations of ions showed clear differences between cyclonic-westerly-dominated periods and others, reflecting higher marine and lower anthropogenic contributions in Atlantic air masses. Westerlies were associated with higher rainfall, higher sea salt concentrations, and lower pollutant concentrations at all sites, while air mass paths exerted additional controls. Westerlies therefore have continued to favour higher sea salt fluxes, whereas emission reductions are increasingly leading to positive correlations between westerlies and pollutant fluxes. Our results also suggest a shift from the influence of anthropogenic emissions to natural emissions (e.g., sea salt) and climate forcing as they are transported under relatively cleaner conditions to the UK. Westerlies have been relatively frequent over the ECN monitoring period, but longer-term cyclicity in these weather types suggests that current contributions to precipitation may not be sustained over coming years.

Estimation of the impacts of atmospheric nitrogen (N) deposition on ecosystems and biodiversity is a research imperative. Analyses of large-scale spatial gradients, where an observed response is correlated with measured or modelled deposition, have been an important source of evidence. A number of problems beset this approach. For example, if responses are spatially aggregated then treating each location as statistically independent can lead to biased confidence intervals and a greater probably of false positive results. Using methods that account for residual spatial autocorrelation, Pescott & Jitlal (2020) re-analysed two large-scale spatial gradient datasets from Britain where modelled N deposition at 5 × 5 km resolution had been previously correlated with species richness in small quadrats. They found that N deposition effects were weaker than previously demonstrated leading them to conclude that “previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated”. We use a simulation study to show that their conclusion is unreliable despite them recognising that an influential fraction of the residual spatially structured variation could itself be attributable to N deposition. This arises because the covariate used was modelled N deposition at 5 × 5 km resolution leaving open the possibility that measured or modelled N deposition at finer resolutions could explain more variance in the response. Explicitly treating this as spatially auto-correlated error ignores this possibility and leads directly to their unreliable conclusion. We further demonstrate the plausibility of this scenario by showing that significant variation in N deposition at the 1 km square resolution is indeed averaged at 5 × 5 km resolution. Further analyses are required to explore whether estimation of the size of the N deposition effect on plant species richness and other measures of biodiversity is indeed dependent on the accuracy and hence measurement error of the N deposition covariate. Until then the conclusions of Pescott & Jitlal (2020) should be considered premature.

Abstract. 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 300 1 km squares throughout Wales. The field survey capitalised upon the 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 and 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. Landscape features, 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, https://doi.org/10.5285/71d3619c-4439-4c9e-84dc-3ca873d7f5cc; Topsoil physico-chemical properties, https://doi.org/10.5285/0fa51dc6-1537-4ad6-9d06-e476c137ed09; Topsoil meso-fauna, https://doi.org/10.5285/1c5cf317-2f03-4fef-b060-9eccbb4d9c21; Topsoil particle size distribution https://doi.org/10.5285/d6c3cc3c-a7b7-48b2-9e61-d07454639656; Headwater stream quality metrics, https://doi.org/10.5285/e305fa80-3d38-4576-beef-f6546fad5d45 ; Pond quality metrics, https://doi.org/10.5285/687b38d3-2278-41a0-9317-2c7595d6b882; Insect pollinator and flower data, https://doi.org/10.5285/3c8f4e46-bf6c-4ea1-9340-571fede26ee8; Bird counts, https://doi.org/10.5285/31da0a94-62be-47b3-b76e-4bdef3037360.

Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications.

they apparently fulfil standard BS EN ISO 5362:2019, Anaesthetic reservoir bags. It is worrying that a product which is so different from the original can comply with industry standards and be widely distributed for use in patients without formal clinical assessment. Levin identified that clinical performance needs to be recognised when manufacturing medical equipment and that adherence to industrial standards in isolation may compromise safety [5]. Studies by both Tan and Schilly clearly show that experienced paediatric anaesthetists can detect over 80% of tracheal occlusions by the feel of the bag alone [2,3]. Certainly it would be difficult to describe within a standard how a device feels after years of clinical experience. We would therefore advocate greater engagement between clinician and manufacturer to deliver a product that addresses manufacturing constraints yet maintains clinical performance.