Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Picea abies Karst

European beech (Fagus sylvatica L.) and Norway spruce (Picea abies Karst.) are two of the most ecologically and economically important forest tree species in Europe. These two species co-occur in many locations in Europe, leading to direct competition for canopy space. Foliage characteristics of two naturally regenerated pure stands of beech and spruce with fully closed canopies were contrasted to assess the dynamic relationship between foliage adaptability to shading, stand LAI and tree growth. We found that individual leaf size is far more conservative in spruce than in beech. Individual leaf and needle area was larger at the top than at the bottom of the canopy in both species. Inverse relationship was found for specific leaf area (SLA), highest SLA values were found at lowest light availability under the canopy. There was no difference in leaf area index (LAI) between the two stands, however LAI increased from 10.8 to 14.6 m2m-2 between 2009 and 2011. Dominant trees of both species were more efficient in converting foliage mass or area to produce stem biomass, although this relationship changed with age and was species-specific. Overall, we found larger foliage plasticity in beech than in spruce in relation to light conditions, indicating larger capacity to exploit niche openings.

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales.

Evaluation of moisture sources for the Central European summer flood of May/June 2013 based on regional climate model simulations

Heavy precipitation affected Central Europe in May/June 2013, triggering damaging floods both on the Danube and the Elbe rivers. Based on a modelling approach with COSMO-CLM, moisture fluxes, backward trajectories, cyclone tracks and precipitation fields are evaluated for the relevant time period 30 May–2 June 2013. We identify potential moisture sources and quantify their contribution to the flood event focusing on the Danube basin through sensitivity experiments: Control simulations are performed with undisturbed ERA-Interim boundary conditions, while multiple sensitivity experiments are driven with modified evaporation characteristics over selected marine and land areas. Two relevant cyclones are identified both in reanalysis and in our simulations, which moved counter-clockwise in a retrograde path from Southeastern Europe over Eastern Europe towards the northern slopes of the Alps. The control simulations represent the synoptic evolution of the event reasonably well. The evolution of the precipitation event in the control simulations shows some differences in terms of its spatial and temporal characteristics compared to observations. The main precipitation event can be separated into two phases concerning the moisture sources. Our modelling results provide evidence that the two main sources contributing to the event were the continental evapotranspiration (moisture recycling; both phases) and the North Atlantic Ocean (first phase only). The Mediterranean Sea played only a minor role as a moisture source. This study confirms the importance of continental moisture recycling for heavy precipitation events over Central Europe during the summer half year.

Seasonality of westerly moisture transport in the East Asian summer monsoon and its implications for interpreting precipitation δ18O

East Asian summer monsoon (EASM) rainfall impacts the world's most populous regions. Accurate EASM rainfall prediction necessitates robust paleoclimate reconstructions from proxy data and quantitative linkage to modern climatic conditions. Many precisely dated oxygen isotope records from Chinese stalagmites have been interpreted as directly reflecting past EASM rainfall amount variability, but recent research suggests that such records instead integrate multiple hydroclimatic processes. Using a Lagrangian precipitation moisture source diagnostic, we demonstrate that EASM rainfall is primarily derived from the Indian Ocean. Conversely, Pacific Ocean moisture export peaks during winter, and the moisture uptake area does not differ significantly between summer and winter and is thus a minor contributor to monsoonal precipitation. Our results are substantiated by an accurate reproduction of summer and winter spatial rainfall distributions across China. We also correlate modern EASM rainfall oxygen isotope ratios with instrumental rainfall amount and our moisture source data. This analysis reveals that the strength of the source effect is geographically variable, and differences in atmospheric moisture transport may significantly impact the isotopic signature of EASM rainfall at the Hulu, Dongge, and Wanxiang Cave sites. These results improve our ability to isolate the rainfall amount signal in paleomonsoon reconstructions and indicate that precipitation across central and eastern China will directly respond to variability in Indian Ocean moisture supply.

Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants

Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world's men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world's poorest regions, especially in south Asia.