Abstract
AbstractBiodiversity, 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.
Topics

No keywords indexed for this article. Browse by subject →

References
35
[2]
Diversity of Planktonic Foraminifera in Deep-Sea Sediments

Wolfgang H. Berger, Frances L. Parker

Science 10.1126/science.168.3937.1345
[13]
Hooper D. "Structural equation modelling: guidelines for determining model fit" Elect. J. Bus. Res. Methods (2008)
[19]
Magurran A. E. (2004)
[21]
May R. M. (1975)
[22]
McCune B. (2002)
[24]
Opposite trends in response for the Shannon and Simpson indices of landscape diversity

Harini Nagendra

Applied Geography 10.1016/s0143-6228(02)00002-4
[25]
J. Oksanen F. G. Blanchet R. Kindt P. Legendre P. R. Minchin R. B. O'Hara 2012
[27]
R Core Team (2013)
[28]
A Mathematical Theory of Communication

C. E. Shannon

Bell System Technical Journal 10.1002/j.1538-7305.1948.tb01338.x
[29]
Measurement of Diversity

E. H. SIMPSON

Nature 10.1038/163688a0
[33]
EVOLUTION AND MEASUREMENT OF SPECIES DIVERSITY

R. H. Whittaker

TAXON 10.2307/1218190
Metrics
886
Citations
35
References
Details
Published
Aug 28, 2014
Vol/Issue
4(18)
Pages
3514-3524
License
View
Authors
Funding
Deutsche Forschungsgemeinschaft Award: 1374
Cite This Article
E. Kathryn Morris, Tancredi Caruso, François Buscot, et al. (2014). Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecology and Evolution, 4(18), 3514-3524. https://doi.org/10.1002/ece3.1155
Related

You May Also Like