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Climate change is a significant driver of contemporary biodiversity change. Ecological monitoring schemes can be crucial in highlighting its consequences, but connecting and interpreting observed climatic and ecological changes demands an understanding of monitored locations' exposure to climate change. Generalising from trends in monitored sites to habitats also requires an assessment of how closely sampled locations' climate change trajectories mirror those of wider ecosystems. Such assessments are rare but vital for drawing robust ecological conclusions.Focusing on the UK, we generated a metric of climate change exposure by quantifying the change in observed historical (1901–2019) and predicted future (2021–2080, pessimistic emissions scenario) conditions. We then assessed habitat‐specific climate change exposure by overlaying the resulting data with maps of contemporary (2019) land cover. Finally, we compared patterns of climate change exposure in locations sampled by ecological monitoring schemes to random samples from wider habitats.The UK's climate changed significantly between the early 20th century and the last decade, and is predicted to undergo even greater changes (including the development of Iberian/Mediterranean climate types in places) into the 21st century. Climate change exposure is unevenly distributed: regionally, it falls more in southern, central and eastern England; locally, it is greater at higher‐elevation locations than nearby areas at lower elevations.Areas with contemporary arable and horticulture, urban, calcareous grassland and suburban land cover are predicted to experience the greatest overall climatic change, though other habitats experienced relatively greater change than these in the first half of the 20th century.The extent to which locations sampled by ecological monitoring schemes represent broader habitat‐level gradients of climate change exposure varies. Monitored sites' coverage of wider trends is heterogeneous across habitats, time periods and schemes.Policy implications. UK ecological monitoring schemes can effectively, though variably, capture the effects of climate change on habitats. To improve their performance, climate change could be explicitly included in the design of such programmes. Additionally, our findings on how effectively different datasets represent wider patterns of climate change are crucial for informing syntheses of ecological change connected to shifting atmospheric conditions. [ABSTRACT FROM AUTHOR]

Few methods are available for transparently combining different evidence streams for chemical risk assessment to reach an integrated conclusion on the probability of causation. Hence, the UK Committees on Toxicity (COT) and on Carcinogenicity (COC) have reviewed current practice and developed guidance on how to achieve this in a transparent manner, using graphical visualisation. All lines of evidence, including toxicological, epidemiological, new approach methodologies, and mode of action should be considered, taking account of their strengths/weaknesses in their relative weighting towards a conclusion on the probability of causation. A qualitative estimate of the probability of causation is plotted for each line of evidence and a combined estimate provided. Guidance is provided on integration of multiple lines of evidence for causation, based on current best practice. Qualitative estimates of probability for each line of evidence are plotted graphically. This ensures a deliberative, consensus conclusion on likelihood of causation is reached. It also ensures clear communication of the influence of the different lines of evidence on the overall conclusion on causality. Issues on which advice from the respective Committees is sought varies considerably, hence the guidance is designed to be sufficiently flexible to meet this need. [Display omitted] • Approach to improve the consistency, transparency and communication of current practice of scientific advisory committees. • Emphasis is on weight of evidence and integration of different evidence streams, where all evidence is considered. • Visualisation tool is proposed, to help communicate the overall conclusion and contribution of different lines of evidence. • The conclusion on causality and its graphical representation is qualitative, and based on expert judgement. • Collaboration and ongoing dialogue between the different disciplines in risk assessment are strongly advised. [ABSTRACT FROM AUTHOR]

Qualitative researchers are increasingly reanalyzing and synthesizing data sets from different studies, and this method has now been used across trials to inform trial methodology and delivery. Despite this work, however, limited guidance exists about how this method should be employed. This article details an example in which interview data collected during three primary care depression trials were brought together to explore trial participants' study and treatment journeys. It details the process involved and the decisions made. It also presents findings from this synthesis to illustrate how this method can be used to inform the development of future trials and complex interventions, through raising questions about how researchers currently define and design treatment arms and indicating what factors may improve or hinder participants' engagement with their allocated treatment. [ABSTRACT FROM AUTHOR]

Nearly 5000 chamber measurements of CH4 flux were collated from 21 sites across the United Kingdom, covering a range of soil and vegetation types, to derive a parsimonious model that explains as much of the variability as possible, with the least input requirements. Mean fluxes ranged from −0.3 to 27.4 nmol CH4 m−2 s−1, with small emissions or low rates of net uptake in mineral soils (site means of −0.3 to 0.7 nmol m−2 s−1) and much larger emissions from organic soils (site means of −0.3 to 27.4 nmol m−2 s−1). Less than half of the observed variability in instantaneous fluxes could be explained by independent variables measured. The reasons for this include measurement error, stochastic processes and, probably most importantly, poor correspondence between the independent variables measured and the actual variables influencing the processes underlying methane production, transport and oxidation. When temporal variation was accounted for, and the fluxes averaged at larger spatial scales, simple models explained up to ca. 75% of the variance in CH4 fluxes. Soil carbon, peat depth, soil moisture and pH together provided the best sub-set of explanatory variables. However, where plant species composition data were available, this provided the highest explanatory power. Linear and nonlinear models generally fitted the data equally well, with the exception that soil moisture required a power transformation. To estimate the impact of changes in peatland water table on CH4 emissions in the United Kingdom, an emission factor of +0.4 g CH4 m−2 yr−1 per cm increase in water table height was derived from the data. [ABSTRACT FROM AUTHOR]

Abstract: It is well established that there exist substantial area-level socio-demographic variations in population health. However, area-level associations between deprivation and health cannot necessarily be interpreted as place effects on individual health. We demonstrate how recently developed statistical models for combining individual and aggregate data can help to separate the effects of place of residence and personal circumstances. We apply these to two health outcomes: risk of hospitalisation for cardiovascular disease (CVD) and risk of self-reported limiting long-term illness (LLTI). A combination of small-area data from UK hospital episode statistics and the UK census and individual data from the Health Survey for England are analysed, using a new multilevel modelling method termed hierarchical related regression (HRR). The standard multilevel model for place and health explains outcomes from individual data in terms of individual and area-level characteristics. HRR models increase precision by also explaining population aggregate outcomes, in terms of the same predictors. Aggregate outcomes are modelled by averaging the individual-level exposure–outcome relationship over the area, which can alleviate the ecological bias associated with interpreting the relationship between aggregate quantities as an individual-level relationship. We find that there are associations between area-level deprivation indicators and both area-level rates of hospital admission for CVD and area-level rates of LLTI. Multilevel models fitted to the individual data alone had insufficient power to determine whether these associations were due to compositional or contextual effects. Using HRR models which incorporate area-level outcomes in addition to individual outcomes, we found that for CVD, the area-level differences were mostly explained by individual-level effects, in particular the increased risk for individuals from non-white ethnic backgrounds. In contrast, there remained a significant association between LLTI and area-level deprivation even after adjusting for the significant increased risk associated with individual-level ethnicity and income. Our study illustrates that extending multilevel models to incorporate both individual and area-level outcomes increases power to distinguish between contextual and compositional effects. [Copyright &y& Elsevier]