Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests

There is considerable evidence that biodiversity promotes multiple ecosystem functions (multifunctionality), thus ensuring the delivery of ecosystem services important for human well-being. However, the mechanisms underlying this relationship are poorly understood, especially in natural ecosystems. We develop a novel approach to partition biodiversity effects on multifunctionality into three mechanisms and apply this to European forest data. We show that throughout Europe, tree diversity is positively related with multifunctionality when moderate levels of functioning are required, but negatively when very high function levels are desired. For two well-known mechanisms, ‘complementarity’ and ‘selection’, we detect only minor effects on multifunctionality. Instead a third, so far overlooked mechanism, the ‘jack-of-all-trades’ effect, caused by the averaging of individual species effects on function, drives observed patterns. Simulations demonstrate that jack-of-all-trades effects occur whenever species effects on different functions are not perfectly correlated, meaning they may contribute to diversity–multifunctionality relationships in many of the world’s ecosystems.

Incidence and predictors of cutaneous manifestations during the early course of systemic sclerosis: a 10-year longitudinal study from the EUSTAR database.

OBJECTIVES To longitudinally map the onset and identify risk factors for skin sclerosis and digital ulcers (DUs) in patients with systemic sclerosis (SSc) from an early time point after the onset of Raynaud's phenomenon (RP) in the European Scleroderma Trials and Research (EUSTAR) cohort. METHODS 695 patients with SSc with a baseline visit within 1 year after RP onset were followed in the prospective multinational EUSTAR database. During the 10-year observation period, cumulative probabilities of cutaneous lesions were assessed with the Kaplan-Meier method. Cox proportional hazards regression analysis was used to evaluate risk factors. RESULTS The median modified Rodnan skin score (mRSS) peaked 1 year after RP onset, and was 15 points. The 1-year probability to develop an mRSS ≥2 in at least one area of the arms and legs was 69% and 25%, respectively. Twenty-five per cent of patients developed diffuse cutaneous involvement in the first year after RP onset. This probability increased to 36% during the subsequent 2 years. Only 6% of patients developed diffuse cutaneous SSc thereafter. The probability to develop DUs increased to a maximum of 70% at the end of the 10-year observation. The main factors associated with diffuse cutaneous SSc were the presence of anti-RNA polymerase III autoantibodies, followed by antitopoisomerase autoantibodies and male sex. The main factor associated with incident DUs was the presence of antitopoisomerase autoantibodies. CONCLUSION Early after RP onset, cutaneous manifestations exhibit rapid kinetics in SSc. This should be accounted for in clinical trials aiming to prevent skin worsening.

Contrasting growth and water use efficiency after thinning in mixed Abies pinsapo–Pinus pinaster–Pinus sylvestris forests

Foresters frequently lack sufficient information about thinning intensity effects to optimize semi-natural forest management and their effects and interaction with climate are still poorly understood. In an Abies pinsapo–Pinus pinaster–Pinus sylvestris forest with three thinning intensities, a dendrochronologial approach was used to evaluate the short-term responses of basal area increment (BAI), carbon isotope (δ13C) and water use efficiency (iWUE) to thinning intensity and climate. Thinning generally increased BAI in all species, except for the heavy thinning in P. sylvestris. Across all the plots, thinning increased 13C-derived water-use efficiency on average by 14.49% for A. pinsapo, 9.78% for P. sylvestris and 6.68% for P. pinaster, but through different ecophysiological mechanisms. Our findings provide a robust mean of predicting water use efficiency responses from three coniferous species exposed to different thinning strategies which have been modulated by climatic conditions over time.

Days spent in acute care hospitals at the end of life of cancer patients in four Swiss cantons: a retrospective database study (SAKK 89/09).

Number of days spent in acute hospitals (DAH) at the end of life is regarded as an important care quality indicator for cancer patients. We analysed DAH during 90 days prior to death in patients from four Swiss cantons. Claims data from an insurance provider with about 20% market share and patient record review identified 2086 patients as dying of cancer. We calculated total DAH per patient. Multivariable generalised linear modelling served to evaluate potential explanatory variables. Mean DAH was 26 days. In the multivariable model, using complementary and alternative medicine (DAH = 33.9; +8.8 days compared to non-users) and canton of residence (for patient receiving anti-cancer therapy, Zürich DAH = 22.8 versus Basel DAH = 31.4; for other patients, Valais DAH = 22.7 versus Ticino DAH = 33.7) had the strongest influence. Age at death and days spent in other institutions were additional significant predictors. DAH during the last 90 days of life of cancer patients from four Swiss cantons is high compared to most other countries. Several factors influence DAH. Resulting differences are likely to have financial impact, as DAH is a major cost driver for end-of-life care. Whether they are supply- or demand-driven and whether patients would prefer fewer days in hospital remains to be established.

Modeling postglacial vegetation dynamics of temperate forests on the Olympic Peninsula (WA, USA) with special regard to snowpack

Past and future forest composition and distribution in temperate mountain ranges is strongly influenced by temperature and snowpack. We used LANDCLIM, a spatially explicit, dynamic vegetation model, to simulate forest dynamics for the last 16,000 years and compared the simulation results to pollen and macrofossil records at five sites on the Olympic Peninsula (Washington, USA). To address the hydrological effects of climate-driven variations in snowpack on simulated forest dynamics, we added a simple snow accumulation-and-melt module to the vegetation model and compared simulations with and without the module. LANDCLIM produced realistic present-day species composition with respect to elevation and precipitation gradients. Over the last 16,000 years, simulations driven by transient climate data from an atmosphere-ocean general circulation model (AOGCM) and by a chironomid-based temperature reconstruction captured Late-glacial to Late Holocene transitions in forest communities. Overall, the reconstruction-driven vegetation simulations matched observed vegetation changes better than the AOGCM-driven simulations. This study also indicates that forest composition is very sensitive to snowpack-mediated changes in soil moisture. Simulations without the snow module showed a strong effect of snowpack on key bioclimatic variables and species composition at higher elevations. A projected upward shift of the snow line and a decrease in snowpack might lead to drastic changes in mountain forests composition and even a shift to dry meadows due to insufficient moisture availability in shallow alpine soils.

Special Tax Treatment as Trade Policy: A Database on Export Processing and Special Economic Zones

Many countries treat income generated via exports favourably, especially when production takes places in special zones known as export processing zones (EPZs). EPZs can be defined as specific, geographically defined zones or areas that are subject to special administration and that generally offer tax incentives, such as duty‐free imports when producing for export, exemption from other regulatory constraints linked to import for the domestic market, sometimes favourable treatment in terms of industrial regulation, and the streamlining of border clearing procedures. We describe a database of WTO Members that employ special economic zones as part of their industrial policy mix. This is based on WTO notification and monitoring through the WTO’s trade policy review mechanism (TPRM), supplemented with information from the ILO, World Bank, and primary sources. We also provide some rough analysis of the relationship between use of EPZs and the carbon intensity of exports, and relative levels of investment across countries with and without special zones.

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^