2006, Paris:

• Finding the Minimum Sample Richness (MSR) for multivariate analyses: implications for palaeoecology. Michael Bedward.

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

Michael Bedward and Kenny J Travouillon

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 (Hammer & Harper 2006), and presence/absence data is often the only type of data that can be used to provide an estimate of ancient biodiversity. We propose a new technique (using multivariate analyses) to assess whether palaeontological presence/absence data are statistically representative of original life assemblages. Artificially generated species lists are used to make parent and subset lists which are then compared using a cluster analysis to find the minimum sample richness (MSR) with a 95% confidence of correctly clustering the parent and subset lists. Several commonly used similarity indexes (Dice, Jaccard, Simpson and Raup-Crick) were investigated. Of these, the Raup-Crick index required the lowest MSR, i.e. it performed best at correctly clustering subset lists with their respective parents at low sample species richness. MSR can be found by our graphs for presence/absence data provided absolute species richness and the beta diversity can be estimated.