Molecular- and pollen-based vegetation analysis in lake sediments from central Scandinavia

Plant and animal biodiversity can be studied by obtaining DNA directly from the environment. This new approach in combination with the use of generic barcoding primers (metabarcoding) has been suggested as complementary or alternative to traditional biodiversity monitoring in ancient soil sediments. However, the extent to which metabarcoding truly reflects plant composition remains unclear, as does its power to identify species with no pollen or macrofossil evidence. Here, we compared pollen-based and metabarcoding approaches to explore the Holocene plant composition around two lakes in central Scandinavia. At one site, we also compared barcoding results with those obtained in earlier studies with species-specific primers. The pollen analyses revealed a larger number of taxa (46), of which the majority (78%) was not identified by metabarcoding. The metabarcoding identified 14 taxa (MTUs), but allowed identification to a lower taxonomical level. The combined analyses identified 52 taxa. The barcoding primers may favour amplification of certain taxa, as they did not detect taxa previously identified with species-specific primers. Taphonomy and selectiveness of the primers are likely the major factors influencing these results. We conclude that metabarcoding from lake sediments provides a complementary, but not an alternative, tool to pollen analysis for investigating past flora. In the absence of other fossil evidence, metabarcoding gives a local and important signal from the vegetation, but the resulting assemblages show limited capacity to detect all taxa, regardless of their abundance around the lake. We suggest that metabarcoding is followed by pollen analysis and the use of species-specific primers to provide the most comprehensive signal from the environment. © 2013 Blackwell Publishing Ltd.

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 (D1), Simpson's dominance (D2), 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.