Estimating species richness

Measuring species richness is an essential objective for many community ecologists and conservation biologists. The number of species in a local assemblage is an intuitive and natural index of community structure, and patterns of species richness have been measured at both small (e.g. Blake & Loiselle 2000) and large (e.g. Rahbek & Graves 2001) spatial scales. Many classic models in community ecology, such as the MacArthur–Wilson equilibrium model (MacArthur & Wilson 1967) and the intermediate disturbance hypothesis (Connell 1978), as well as more recent models of neutral theory (Hubbell 2001), metacommunity structure (Holyoak et al. 2005), and biogeography (Gotelli et al. 2009) generate quantitative predictions of the number of coexisting species. To make progress in modelling species richness, these predictions need to be compared with empirical data. In applied ecology and conservation biology, the number of species that remain in a community represents the ultimate ‘scorecard’ in the fight to preserve and restore perturbed communities (e.g. Brook et al. 2003). Yet, in spite of our familiarity with species richness, it is a surprisingly difficult variable to measure. Almost without exception, species richness can be neither accurately measured nor directly estimated by observation because the observed number of species is a downward-biased estimator for the complete (total) species richness of a local assemblage. Hundreds of papers describe statistical methods for correcting this bias in the estimation of species richness (see also Chapter 3), and special protocols and methods have been developed for estimating species richness for particular taxa (e.g. Agosti et al. 2000). Nevertheless, many recent studies continue to ignore some of the fundamental sampling and measurement problems that can compromise the accurate estimation of species richness (Gotelli & Colwell 2001). In this chapter we review the basic statistical issues involved with species richness estimation. Although a complete review of the subject is beyond the scope of this chapter, we highlight sampling models for species richness that account for undersampling bias by adjusting or controlling for differences in the number of individuals and the number of samples collected (rarefaction) as well as models that use abundance or incidence distributions to estimate the number of undetected species (estimators of asymptotic richness).