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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.

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.

Abstract The Hikurangi Margin east of New Zealand's North Island hosts an extensive gas hydrate province with numerous gas hydrate accumulations related to the faulted structure of the accretionary wedge. One such hydrate feature occurs in a small perched upper‐slope basin known as Urutī Basin. We investigated this hydrate accumulation by combining a long‐offset seismic line (10‐km‐long receiver array) with a grid of high‐resolution seismic lines acquired with a 600‐m‐long hydrophone streamer. The long‐offset data enable quantitative velocity analysis, while the high‐resolution data constrain the three‐dimensional geometry of the hydrate accumulation. The sediments in Urutī Basin dip landward due to ongoing deformation of the accretionary wedge. These strata are clearly imaged in seismic data where they cross a distinct bottom simulating reflection (BSR) that dips counterintuitively in the opposite direction to the regional dip of the seafloor. BSR‐derived heat flow estimates reveal a distinct heat flow anomaly that coincides spatially with the upper extent of a landward‐verging thrust fault. We present a conceptual model of this gas hydrate system that highlights the roles of fault‐controlled fluid flow at depth merging into strata‐controlled fluid flow into the hydrate stability zone. The result is a layer‐constrained accumulation of concentrated gas hydrate in the dipping strata. Our study provides new insight into the interplay between deep faulting, fluid flow and gas hydrate formation within an active accretionary margin.

Abstract Marine multichannel and wide‐angle seismic data constrain the distribution of seamounts, sediment cover sequence and crustal structure along a 460 km margin‐parallel transect of the Hikurangi Plateau. Seismic reflection data reveals five seamount up‐to 4.5 km high and 35–75 km wide, with heterogeneous internal velocity structure. Sediment cover decreases south‐to‐north from ∼4.5 km to ∼1–2 km. The Hikurangi Plateau crust (VP 5.5–7.5 km/s) is 11 ± 1 km thick in the south, but thins by 3–4 km further north (∼7–8 km). Gravity models constructed along two seismic lines show the reduction in crustal thickness persists further east, coinciding with a bathymetric scarp. Gravity data suggest the transition in crustal thickness may reflect spatial variability in deformation and lithospheric extension associated with plateau breakup. Variability in the thickness of subducting crust may contribute to differences in megathrust geometry, upper‐plate stress state and high‐rates of contraction and uplift along the southern Hikurangi margin.

Abstract Background Since climate change, pandemics and population mobility are challenging healthcare systems, an empirical and integrative research to studying and help improving the health systems resilience is needed. We present an interdisciplinary and mixed-methods research protocol, ClimHB, focusing on vulnerable localities in Bangladesh and Haiti, two countries highly sensitive to global changes. We develop a protocol studying the resilience of the healthcare system at multiple levels in the context of climate change and variability, population mobility and the Covid-19 pandemic, both from an institutional and community perspective. Methods The conceptual framework designed is based on a combination of Levesque’s Health Access Framework and the Foreign, Commonwealth and Development Office’s Resilience Framework to address both outputs and the processes of resilience of healthcare systems. It uses a mixed-method sequential exploratory research design combining multi-sites and longitudinal approaches. Forty clusters spread over four sites will be studied to understand the importance of context, involving more than 40 healthcare service providers and 2000 households to be surveyed. We will collect primary data through questionnaires, in-depth and semi-structured interviews, focus groups and participatory filming. We will also use secondary data on environmental events sensitive to climate change and potential health risks, healthcare providers’ functioning and organisation. Statistical analyses will include event-history analyses, development of composite indices, multilevel modelling and spatial analyses. Discussion This research will generate inter-disciplinary evidence and thus, through knowledge transfer activities, contribute to research on low and middle-income countries (LMIC) health systems and global changes and will better inform decision-makers and populations.

Abstract 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.

This study describes the apparent discontinuation of cholera transmission in Haiti since February 2019. Because vulnerabilities persist and vaccination remains limited, our findings suggest that case-area targeted interventions conducted by rapid response teams played a key role. We question the presence of environmental reservoirs in Haiti and discuss progress toward elimination.

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.

Centre Department, Haiti, was the origin of a major cholera epidemic during 2010–2019. Although no fine-scale spatial delineation is officially available, we aimed to analyze determinants of cholera at the local level and identify priority localities in need of interventions. After estimating the likely boundaries of 1,730 localities by using Voronoi polygons, we mapped 5,322 suspected cholera cases reported during January 2015–September 2016 by locality alongside environmental and socioeconomic variables. A hierarchical clustering on principal components highlighted 2 classes with high cholera risk: localities close to rivers and unimproved water sources (standardized incidence ratio 1.71, 95% CI 1.02–2.87; p = 0.04) and urban localities with markets (standardized incidence ratio 1.69, 95% CI 1.25–2.29; p = 0.0006). Our analyses helped identify and characterize areas where efforts should be focused to reduce vulnerability to cholera and other waterborne diseases; these methods could be used in other contexts.

Geological records from the Antarctic margin offer direct evidence of environmental variability at high southern latitudes and provide insight regarding ice sheet sensitivity to past climate change. The early to mid-Miocene (23-14 Mya) is a compelling interval to study as global temperatures and atmospheric CO2 concentrations were similar to those projected for coming centuries. Importantly, this time interval includes the Miocene Climatic Optimum, a period of global warmth during which average surface temperatures were 3-4 °C higher than today. Miocene sediments in the ANDRILL-2A drill core from the Western Ross Sea, Antarctica, indicate that the Antarctic ice sheet (AIS) was highly variable through this key time interval. A multiproxy dataset derived from the core identifies four distinct environmental motifs based on changes in sedimentary facies, fossil assemblages, geochemistry, and paleotemperature. Four major disconformities in the drill core coincide with regional seismic discontinuities and reflect transient expansion of grounded ice across the Ross Sea. They correlate with major positive shifts in benthic oxygen isotope records and generally coincide with intervals when atmospheric CO2 concentrations were at or below preindustrial levels (∼280 ppm). Five intervals reflect ice sheet minima and air temperatures warm enough for substantial ice mass loss during episodes of high (∼500 ppm) atmospheric CO2 These new drill core data and associated ice sheet modeling experiments indicate that polar climate and the AIS were highly sensitive to relatively small changes in atmospheric CO2 during the early to mid-Miocene.

Abstract Background Religious pilgrimages are among the anthropogenic factors known to be associated with the transmission of diarrheal diseases, such as cholera. This ecological study aimed to describe the evolution of cholera and assess the relationship between the implementation of the ‘coup de poing’ strategy during the patron saint festivities and the incidence of cholera in the three communes of Cabaret, Carrefour, and Croix-des-Bouquets in Haiti in 2017. Methods An epidemiological curve was produced to illustrate the evolution of cholera at the communal level. Generalized linear models assuming a Poisson distribution were used to weight the annual cholera incidence of communal sections against variables such as the number of patronal festivities, population density and annual precipitation rates. The number of cases in the week of the festivity as well as one and 2 weeks later was weighted against patronal festivities and weekly precipitation rates. Results In total, 3633 suspected cholera cases were continuously reported in three communes in Haiti (Cabaret, Carrefour, Croix-des-bouquets) during the 52-epidemiological week period in 2017. After controlling for rainfall and population density, the implementation of the ‘coup de poing’ strategy during the patron saint festivities was associated with a significant reduction in cholera incidence of 57.23% [PR = 0.4277 (97.5% CI: 0.2798–0.6193), p = 0.0000244]. The implementation of the strategy was associated with a reduction in cholera incidence of 25.41% 1 week following patronal festivities. Conclusion This study showed a continuous presence of cholera in three communes in Haiti in 2017 and an association between the implementation of the ‘coup de poing’ strategy during patronal festivities and a reduction in cholera incidence. The findings imply that the multi-partner ‘coup de poing’ strategy may have contributed to the reduced cholera incidence following patron saint festivities and in Ouest department in Haiti in 2017.

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.

Abstract The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km2 (Zi) is unobserved, but both ground survey and remote sensing can be used to estimate Zi. The model allows the relationship between remote sensing data and Zi to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.

Summary: Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists, and representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency, and reproducibility in science. The perspective also includes a call to build an international community working in this important area. The Bigger Picture: Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science in addressing grand scientific challenges. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective reports on the outcomes from a summit in this area, attended by 42 researchers selected as leading experts operating at the interface between digital technology and the environmental sciences. The key output of this workshop was the Windermere Accord, a collective statement around what is required to achieve a transformative effect through digital technology based around four key pillars of investigation, namely using technology to tame uncertainty; growing advocates and champions to enable, empower, and influence; embracing a new open and transparent style of science; and enabling integration and sophisticated treatment of feedbacks in complex environmental systems. These pillars all feed into the decision-making processes and are supported by a growing community. Looking forward, the accord also identified a pathway with particular emphasis on building an international, cross-disciplinary community to address the key challenges and achieve the real opportunities around digital technology and the environment.

Summary: In recent years, there has been a drive toward more open, cross-disciplinary science taking center stage. This has presented a number of challenges, including providing research platforms for collaborating scientists to explore big data, develop methods, and disseminate their results to stakeholders and decision makers. We present our vision of a “data science lab” as a collaborative space where scientists (from different disciplines), stakeholders, and policy makers can create data-driven solutions to environmental science's grand challenges. We set out a clear and defined research roadmap to serve as a focal point for an international research community progressing toward a more data-driven and transparent approach to environmental data science, centered on data science labs. This includes ongoing case studies of good practice, with the infrastructural and methodological developments required to enable data science labs to support significant increase in our cross- and trans-disciplinary science capabilities. The Bigger Picture: As we move toward the need for open and cross-disciplinary science, and with an ever-increasing volume of data, there is a critical need to provide research platforms that support the wide variety of users that need to gain knowledge from this data. We present our concept of a “data science lab” as a key contribution in this area. A data science lab is a collaborative, dynamic, and tailorable platform that caters for users at varying levels of abstraction. We illustrate the concept with an initial implementation of a data science lab, drawing on our experiences in the cross-disciplinary field of environmental data science, thus aiming to develop a more data-driven and transparent approach to science. We set out a research roadmap to serve as a focal point for the international research community to take the concept forward and enable data science labs to support the ever-increasing requirement for cross- and trans-disciplinary science capabilities.

Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily.

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings.

Abstract Controlled and natural source seismic data are used to build a 3‐D P wave model for southern North Island, New Zealand, where the Pacific Plate subducts beneath the Australian Plate at a rate of ~41 mm/year. Our analysis reveals an abrupt along‐strike transition in overthrusting plate structure within Cook Strait. Contrasts in properties (Vp, Vp/Vs, and Qs) likely reflects the degree of deformation in the Australian Plate, where the Alpine‐Wairau and Awatere Faults mark the northern boundary of a terrane that has undergone >50° of clockwise vertical‐axis rotation since the early Miocene. Heterogeneity of the crustal transition is likely associated with changes in frictional and elastic properties that may impact elastic stress accumulation and inhibit southward propagation of megathrust earthquakes. Low connectivity of faults in Cook Strait is consistent with the heterogeneity we observe and may promote complex earthquake triggering by lateral stress loading during earthquakes or slow slip events.

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.

An International Ocean Discovery Program (IODP) workshop was held at Sydney University, Australia, from 13 to 16 June 2017 and was attended by 97 scientists from 12 countries. The aim of the workshop was to investigate future drilling opportunities in the eastern Indian Ocean, southwestern Pacific Ocean, and the Indian and Pacific sectors of the Southern Ocean. The overlying regional sedimentary strata are underexplored relative to their Northern Hemisphere counterparts, and thus the role of the Southern Hemisphere in past global environmental change is poorly constrained. A total of 23 proposal ideas were discussed, with ∼  12 of these deemed mature enough for active proposal development or awaiting scheduled site survey cruises. Of the remaining 11 proposals, key regions were identified where fundamental hypotheses are testable by drilling, but either site surveys are required or hypotheses need further development. Refinements are anticipated based upon regional IODP drilling in 2017/2018, analysis of recently collected site survey data, and the development of site survey proposals. We hope and expect that this workshop will lead to a new phase of scientific ocean drilling in the Australasian region in the early 2020s.

The Countryside Survey (CS) of Great Britain (GB) provides a unique and statistically robust series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to provide a baseline for ecological and land use change monitoring in the rural environment of GB, following a stratified random design, based on 1 km squares. Originally, eight random 1 km squares were drawn from each of 32 environmental classes, thus comprising 256 sample squares in the 1978 survey. The number of these sites increased to 382 in 1984, 506 in 1990, 569 in 1998 and 591 in 2007. Detailed information regarding vegetation types and land use was mapped in all five surveys, allowing reporting by defined standard habitat classifications. Additionally, point and linear landscape features (such as trees and hedgerows) are available from all surveys after 1978. From these stratified, randomly located sample squares, information can be converted into national estimates, with associated error terms. Other data, relating to soils, freshwater and vegetation, were also sampled on analogous dates. However, the present paper describes only the surveys of landscape features and habitats. The resulting datasets provide a unique, comprehensive, quantitative ecological coverage of extent and change in these features in GB. Basic results are presented and their implications discussed. However, much opportunity for further analyses remains. Data from each of the survey years are available via the following DOIs: Landscape area data 1978: https://doi.org/10.5285/86c017ba-dc62-46f0-ad13-c862bf31740e, 1984: https://doi.org/10.5285/b656bb43-448d-4b2c-aade-7993aa243ea3, 1990: https://doi.org/10.5285/94f664e5-10f2-4655-bfe6-44d745f5dca7, 1998: https://doi.org/10.5285/1e050028-5c55-42f4-a0ea-c895d827b824, and 2007: https://doi.org/10.5285/bf189c57-61eb-4339-a7b3-d2e81fdde28d; Landscape linear feature data 1984: https://doi.org/10.5285/a3f5665c-94b2-4c46-909e-a98be97857e5, 1990: https://doi.org/10.5285/311daad4-bc8c-485a-bc8a-e0d054889219, 1998: https://doi.org/10.5285/8aaf6f8c-c245-46bb-8a2a-f0db012b2643 and 2007: https://doi.org/10.5285/e1d31245-4c0a-4dee-b36c-b23f1a697f88, Landscape point feature data 1984: https://doi.org/10.5285/124b872e-036e-4dd3-8316-476b5f42c16e, 1990: https://doi.org/10.5285/1481bc63-80d7-4d18-bcba-8804aa0a9e1b, 1998: https://doi.org/10.5285/ed10944f-40c8-4913-b3f5-13c8e844e153 and 2007: https://doi.org/10.5285/55dc5fd7-d3f7-4440-b8a7-7187f8b0550b.

The Countryside Survey (CS) of Great Britain provides a globally unique series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The same sites, with some additional squares, were used for subsequent surveys of vegetation undertaken in 1990, 1998 and 2007, with the intention of future surveys. Other data records include soils, freshwater habitats and invertebrates, and land cover and landscape feature diversity and extents. These data have been recorded in the same locations on analogous dates. However, the present paper describes only the details of the vegetation surveys. The survey design is a series of gridded, stratified, randomly selected 1 km squares taken as representative of classes derived from a statistical environmental classification of Britain. In the 1978 survey, 256 one-kilometre sample squares were recorded, increasing to 506 in 1990, 569 in 1998 and 591 in 2007. Initially each square contained up to 11 dispersed vegetation plots but additional plots were later placed in different features so that eventually up to 36 additional sampling plots were recorded, all of which can be relocated where possible (unless the plot has been lost, for example as a consequence of building work), providing a total of 16 992 plots by 2007. Plots are estimated to have a precise relocation accuracy of 85 %. A range of plots located in different land cover types and landscape features (for example, field boundaries) are included. Although a range of analyses have already been carried out, with changes in the vegetation being related to a range of drivers at local and national scales, there is major potential for further analyses, for example in relation to climate change. Although the precise locations of the plots are restricted, largely for reasons of landowner confidentiality, sample sites are intended to be representative of larger areas, and many potential opportunities for further analyses remain. Data from each of the survey years (1978, 1990, 1998, 2007) are available via the following DOIs: Countryside Survey 1978 vegetation plot data (https://doi.org/10.5285/67bbfabb-d981-4ced-b7e7-225205de9c96), Countryside Survey 1990 vegetation plot data (https://doi.org/10.5285/26e79792-5ffc-4116-9ac7-72193dd7f191), Countryside Survey 1998 vegetation plot data (https://doi.org/10.5285/07896bb2-7078-468c-b56d-fb8b41d47065), Countryside Survey 2007 vegetation plot data (https://doi.org/10.5285/57f97915-8ff1-473b-8c77-2564cbd747bc).

Abstract Fertilization of nitrogen (N)-limited ecosystems by anthropogenic atmospheric nitrogen deposition (Ndep) may promote CO2 removal from the atmosphere, thereby buffering human effects on global radiative forcing. We used the biogeochemical ecosystem model N14CP, which considers interactions among C (carbon), N and P (phosphorus), driven by a new reconstruction of historical Ndep, to assess the responses of soil organic carbon (SOC) stocks in British semi-natural landscapes to anthropogenic change. We calculate that increased net primary production due to Ndep has enhanced detrital inputs of C to soils, causing an average increase of 1.2 kgCm−2 (c. 10%) in soil SOC over the period 1750–2010. The simulation results are consistent with observed changes in topsoil SOC concentration in the late 20th Century, derived from sample-resample measurements at nearly 2000 field sites. More than half (57%) of the additional topsoil SOC is predicted to have a short turnover time (c. 20 years), and will therefore be sensitive to future changes in Ndep. The results are the first to validate model predictions of Ndep effects against observations of SOC at a regional field scale. They demonstrate the importance of long-term macronutrient interactions and the transitory nature of soil responses in the terrestrial C cycle.

Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health, and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting 10 top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems.

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 non-parametric 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.

Background: Following disasters, persons with disabilities (PWD) are especially vulnerable to harm, yet they have commonly been excluded from disaster planning, and their needs have been poorly addressed during disaster relief. Following the 2010 Haiti earthquake, thousands of individuals experienced acute injuries. Many more individuals with preexisting disabilities experienced heightened vulnerability related to considerations including safety, access to services, and meeting basic needs. Objective: The objective of this research was to better understand the perceptions of responders and decision-makers regarding disability and efforts to address the needs of PWD following the 2010 earthquake. Design: We conducted a qualitative study using interpretive description methodology and semistructured interviews with 14 Haitian and 10 international participants who were involved in the earthquake response. Results: Participants identified PWD as being among the most vulnerable individuals following the earthquake. Though some forms of disability received considerable attention in aid efforts, the needs of other PWD did not. Several factors were identified as challenges for efforts to address the needs of PWD including lack of coordination and information sharing, the involvement of multiple aid sectors, perceptions that this should be the responsibility of specialized organizations, and the need to prioritize limited resources. Participants also reported shifts in local social views related to disability following the earthquake. Conclusions: Addressing the needs of PWD following a disaster is a crucial population health challenge and raises questions related to equity and responsibility for non-governmental organizations, governments, and local communities.

In Haiti, where all drugs are available over the counter, self-medication with antibiotics appears as a common practice. Inappropriate use of beta-lactams and macrolides is likely to contribute to the development of antimicrobial resistance. This study aimed to (i) assess the extent of self-medication with antibiotics, (ii) explore the contributing factors (age, gender and educational background) and (iii) identify specific antibiotic drug classes used among patients attending the outpatient clinic of the State University Hospital of Port-au-Prince. A cross-sectional survey among 200 outpatients of the State University Hospital of Port-au-Prince was conducted in December 2014. Face-to-face interviews were conducted using a standardized questionnaire. Parents of pediatric patients were allowed to answer to questions on their behalf. Among the study sample, 45.5% practiced self-medication with antibiotics. It was less prevalent among patients with the highest education level (23.1%; OR: 0.89 (0.5-1.75), p = 0.001). Mild symptoms (28.6%) and vaginal itching (44.4%) were the main reasons for self-medication with antibiotics. Self-medication using amoxicillin was reported by 67.0%. Self-medication with antibiotics is a common practice among Haitian patients and is more common among the less educated. Amoxicillin for urinary tract infections is the most commonly used medication. It is crucial to raise awareness on the dangers of the practice in the population and inforce the current law regarding the use of over the counter antibiotics.

Mycobacterium avium subspecies paratuberculosis (Map) was monitored by quantitative PCR over a range of temporal and spatial scales in the River Tywi catchment. This study shows the persistence of Map over a 10-year period with little change, which correlates with the recognised levels of Johne’s disease in British herds over that period (aim 1). Map was quantified within the river at up to 108 cell equivalents L−1 and was shown to be consistently present when monitored over finer timescales (aim 4). Small wastewater treatment plants where the ingress of human-associated Map might be expected had no significant effect (aim 2). Map was found for the first time to be located in natural river foams providing another route for spread via aerosols (aim 5). This study provides evidence for the environmental continuum of Map from the grazing infected animal via rain driven runoff through field drains and streams into main rivers; with detection at a high frequency throughout the year. Should Map need to be monitored in the future, we recommend that weekly or monthly sampling from a fixed location on a river will capture an adequate representation of the flow dynamics of Map in a catchment (aim 3). The human exposure to Map during this process and its impact on human health remains unquantified.

Atmospheric nitrogen (N) deposition has had detrimental effects on species composition in a range of sensitive habitats, although N deposition can also increase agricultural productivity and carbon storage, and favours a few species considered of importance for conservation. Conservation targets are multiple, and increasingly incorporate services derived from nature as well as concepts of intrinsic value. Priorities vary. How then should changes in a set of species caused by drivers such as N deposition be assessed? We used a novel combination of qualitative semi-structured interviews and quantitative ranking to elucidate the views of conservation professionals specialising in grasslands, heathlands and mires. Although conservation management goals are varied, terrestrial habitat quality is mainly assessed by these specialists on the basis of plant species, since these are readily observed. The presence and abundance of plant species that are scarce, or have important functional roles, emerged as important criteria for judging overall habitat quality. However, species defined as 'positive indicator-species' (not particularly scarce, but distinctive for the habitat) were considered particularly important. Scarce species are by definition not always found, and the presence of functionally important species is not a sufficient indicator of site quality. Habitat quality as assessed by the key informants was rank-correlated with the number of positive indicator-species present at a site for seven of the nine habitat classes assessed. Other metrics such as species-richness or a metric of scarcity were inconsistently or not correlated with the specialists' assessments. We recommend that metrics of habitat quality used to assess N pollution impacts are based on the occurrence of, or habitat-suitability for, distinctive species. Metrics of this type are likely to be widely applicable for assessing habitat change in response to different drivers. The novel combined qualitative and quantitative approach taken to elucidate the priorities of conservation professionals could be usefully applied in other contexts.

No abstract available. doi:10.2204/iodp.sd.14.10.2012

Large areas of Great Britain currently have nitrogen (N) deposition at rates which exceed the thresholds above which there is risk of damage to sensitive components of the ecosystem (critical loads). Previous studies have focussed primarily on the relationship of species richness to nitrogen, whereas here we look at individual species. We used data from two national observation networks over Great Britain to examine the response of individual vascular plant species to N in acid grasslands, calcareous grasslands and heathlands. Presence absence records of individual species, along with mean Ellenberg N scores, within 10 km hectads were modelled against N deposition whilst at the same time controlling for the effects of climate, land use and sulphur deposition using generalised additive models. Ellenberg N showed a significant increase with increasing N deposition in almost all habitats across both surveys indicating increased fertility. Many individual species showed strong relationships with N deposition and clear negative trends in species prevalence to increasing nitrogen were found in all habitats. A number of these species were either habitat dominants or possessed traits known to be influential in controlling ecosystem function. Many community dominants showing significant negative relationships with N deposition highlight a potentially significant loss of function. Some species that showed negative relationships to N showed signs of decline at low levels, far below the current critical load levels. Some species also showed continuous changes as N deposition levels rose above the current critical load values. This work contributes to the growing evidence base suggesting species level impacts at low N deposition values.