Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories

Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon's diversity (H'), Simpson's diversity (D-1), Simpson's dominance (D-2), Simpson's evenness (E), and Berger-Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P.lanceolata molecular and chemical diversity. Including these trait-based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H', even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.

A Swiss paradox? Higher income inequality of municipalities is associated with lower mortality in Switzerland.

It has long been surmised that income inequality within a society negatively affects public health. However, more recent studies suggest there is no association, especially when analyzing small areas. This study aimed to evaluate the effect of income inequality on mortality in Switzerland using the Gini index on municipality level. The study population included all individuals >30 years at the 2000 Swiss census (N = 4,689,545) living in 2,740 municipalities with 35.5 million person-years of follow-up and 456,211 deaths over follow-up. Cox proportional hazard regression models were adjusted for age, gender, marital status, nationality, urbanization, and language region. Results were reported as hazard ratios (HR) with 95 % confidence intervals. The mean Gini index across all municipalities was 0.377 (standard deviation 0.062, range 0.202-0.785). Larger cities, high-income municipalities and tourist areas had higher Gini indices. Higher income inequality was consistently associated with lower mortality risk, except for death from external causes. Adjusting for sex, marital status, nationality, urbanization and language region only slightly attenuated effects. In fully adjusted models, hazards of all-cause mortality by increasing Gini index quintile were HR = 0.99 (0.98-1.00), HR = 0.98 (0.97-0.99), HR = 0.95 (0.94-0.96), HR = 0.91 (0.90-0.92) compared to the lowest quintile. The relationship of income inequality with mortality in Switzerland is contradictory to what has been found in other developed high-income countries. Our results challenge current beliefs about the effect of income inequality on mortality on small area level. Further investigation is required to expose the underlying relationship between income inequality and population health.

Evaluation of multidimensional geriatric assessment as a predictor of mortality and cardiovascular events after transcatheter aortic valve implantation

This study evaluated Multidimensional Geriatric Assessment (MGA) as predictor of mortality and major adverse cardiovascular and cerebral events (MACCE) after transcatheter aortic valve implantation (TAVI).