Finding the Minimum Sample Richness (MSR) for multivariate analyses: implications for palaeoecology

Abstract

Many techniques have been developed to estimate species richness and beta diversity. Those techniques, dependent on sampling, require abundance or presence/absence data. Palaeontological data is by nature incomplete, and presence/absence data is often the only type of data that can be used to provide an estimate of ancient biodiversity. We used a simulation approach to investigate the behaviour of commonly used similarity indices, and the reliability of classifications derived from these indices, when working with incomplete data. We drew samples, of varying number and richness, from artificial species lists, which represented original life assemblages, and calculated error rates for classifications of the parent lists and samples. Using these results, we estimated the Minimum Sample Richness (MSR) needed to achieve 95% classification accuracy. Results were compared for classifications derived from several commonly used similarity indexes (Dice, Jaccard, Simpson and Raup–Crick). MSR was similar for the Dice, Jaccard and Simpson indices. MSR for the Raup–Crick index was often much lower, suggesting that it is preferable for classifying patchy data, however the performance of this index was less stable than the other three in the simulations, which required an even lower MSR. MSR can be found for all presence/absence data from the contour graphs and equations as long as the absolute species richness and the beta diversity can be estimated.

Keywords:

• Sample
• richness
• estimation
• multivariate analysis
• palaeoecology

Keywords:

• 62H30
• 62D05

Acknowledgements

We thank M. Bedward of the New South Wales National Parks and Wildlife Service for writing the R routines essential to this paper. We also want to thank him for his advices and for reading the manuscript. We also would like to thank Gilles Escarguel for providing his help with the fitted functions of MSR. We also want to thank P. Brewer, M. Bassarova, J. Louys (University of New South Wales) and K. Giles Sproule (University of Sydney) for reading and commenting the manuscript. Finally, we thank Bill Sherwin (University of New South Wales) for his advice. Contribution UMR5125-07.020.