SummaryBackgroundThe Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context.MethodsWe used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI).FindingsBetween 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DA...

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.

Aim  We examined whether the community compositions of birds, lizards and small mammals were nested in a fragmented landscape in the Thousand Island Lake, China. We also assessed whether the mechanisms influencing nestedness differed among these taxonomic groups. Location  Thousand Island Lake, China. Methods  Presence/absence matrices were compiled for birds (42 islands) and lizards (42 islands) using line‐transect methods, and for small mammals (14 islands) using live‐trapping methods from 2006 to 2009. Nestedness was analysed using BINMATNEST, and statistical significance was assessed using the conservative null model 3. We used Spearman rank correlations and partial Spearman rank correlations to examine associations of nestedness and habitat variables (area, isolation, habitat diversity and plant richness) as well as life‐history traits (body size, habitat specificity, geographical range size and area requirement) related to species extinction and immigration tendencies. Results  The community compositions of birds, lizards and small mammals were all significantly nested, but the causal factors underlying nestedness differed among taxonomic groups. For birds, island area, habitat specificity and area requirement were significantly correlated with nestedness after controlling for other independent variables. For lizards, habitat heterogeneity was the single best correlate of nestedness. For small mammals, island area, habitat heterogeneity and habitat specificity were significantly correlated with nestedness. The nested patterns of birds, lizards and small mammals were not attributable to passive sampling or selective colonization. Main conclusions  The processes influencing nested patterns differed among taxonomic groups. Nestedness of bird assemblages was driven by selective extinction, and lizard assemblage was caused by habitat nestedness, while nestedness of small mammals resulted from both selective extinction and habitat nestedness. Therefore, we should take taxonomic differences into account when analysing nestedness to develop conservation guidelines and refrain from using single taxa as surrogates for others.

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

Aim The small-island effect (SIE) has become a widely accepted part of the theoretical framework of island biogeography. A major criticism of SIE studies is the exclusion of empty islands from analyses. However, the generality and underlying factors determining the role of empty islands in generating SIEs remain obscure because few published datasets include islands with no species. The aim of this study was thus to evaluate the prevalence and underlying factors determining the role of empty islands in generating SIEs.Location Global. MethodsWe compiled 278 datasets that included empty islands. For each dataset, we compared the fit of a logarithmic model with two breakpoint models separately for all the islands and for datasets excluding empty islands to determine the role of empty islands in generating SIEs. We then employed multinomial logistic regressions and an information-theoretic approach to determine which combination of island characteristics was important in determining the role of empty islands in generating SIEs.Results Among 211 datasets with adequate fits, the exclusion of empty islands changed the evidence for an SIE in 68 cases (32.2%). SIEs were quite prevalent, both for all the islands (104 cases, 49.3%) and for datasets excluding empty islands (73 cases, 34.6%). Our results were not consistent with the hypothesis that excluding empty islands would increase the evidence for an SIE. Model selection and relative variable importance indicated that the number of empty islands, the minimum area of empty islands and area ratio were important variables that determined the role of empty islands in generating SIEs.Main conclusions Our study demonstrates that the effect of empty islands in generating SIEs is quite prevalent. The exclusion of empty islands is thus an important methodological shortcoming for the detection of SIEs. We conclude that, for the robust detection of SIEs, empty islands should not be excluded in future studies.