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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.
Deadwood represents an important structural component of forest ecosystems, where it provides diverse niches for saproxylic biota. Although wood-inhabiting prokaryotes are involved in its degradation, knowledge about their diversity and the drivers of community structure is scarce. To explore the effect of deadwood substrate on microbial distribution, the present study focuses on the microbial communities of deadwood logs from 13 different tree species investigated using an amplicon based deep-sequencing analysis. Sapwood and heartwood communities were analysed separately and linked to various relevant wood physico-chemical parameters. Overall, Proteobacteria, Acidobacteria and Actinobacteria represented the most dominant phyla. Microbial OTU richness and community structure differed significantly between tree species and between sapwood and heartwood. These differences were more pronounced for heartwood than for sapwood. The pH value and water content were the most important drivers in both wood compartments. Overall, investigating numerous tree species and two compartments provided a remarkably comprehensive view of microbial diversity in deadwood.