Print PageBiodiversity, 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, though there is no consensus about which indices are more appropriate and informative. We used data collected around the focal plant Plantago lanceolata on 59 temperate grasslands 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 surrounding the focal plants, arbuscular mycorrhizal fungi, above ground arthropods, below ground insect larva, and P. lanceolata molecular and chemical diversity. We used correlations and principal components analysis (PCA) to determine how well correlated the indices were, and which indices were better at discriminating sites. We also used linear regressions to determine which index, if any, was more affected by a gradient of land use intensity (LUI; as a range of fertilization, grazing and mowing). Finally, we used path analysis to explore relationships between diversities of different organisms and traits.
Results/Conclusions
Diversity indices were generally highly correlated, and the compound diversity measures D1 and D2 were the most effective at discriminating sites. Only S, H’, and D2 for plants were affected, negatively, by LUI; other organisms and traits were completely unaffected. Additional differences between sensitivity of indices were apparent in the path models, where 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.
