Using spatial heterogeneity to extrapolate species richness: a new method tested on Ecuadorian cloud forest birds.
Abstract
For most ecological assemblages, compiling complete inventories of species is difficult, if not impossible. Various methods have been developed to estimate total species richness based on the pattern of richness and relative abundance reflected in a limited sample. We assessed the performance of a recently developed technique, the total-species (T-S) accumulation method, in estimating bird species richness for cloud forest reserves in north-west Ecuador. This technique explicitly integrates the spatial heterogeneity of samples into the estimate of species richness for large areas by grouping samples into subsets based on shared environmental characteristics. We compared the technique's performance with that of more traditional methods of species richness estimation: non-parametric estimators and extrapolations from species-accumulation curves. Existing bird species records for the Ecuadorian cloud forest reserves served as the independent measure of total species richness. Non-parametric estimators of species richness significantly underestimated total species richness. In contrast, extrapolation from the semi-log approximation of T-S curves, using subdivision by habitat type, altitude and first axis detrended correspondence analysis scores, overestimated total species richness, while extrapolation from null T-S models and the standard species-accumulation curve produced the most accurate results. The T-S method overestimated species richness for our 'hyper-diverse' assemblage. This was probably because it adjusted the estimates upwards to account for beta-diversity, when this diversity was already captured in the design of the site selection scheme. Nevertheless, it provided a quantitative measure of the beta-diversity produced by environmental and compositional gradients. Moreover, our test indicated that factors such as sampling effort and species abundance distributions may be more important for accurate species richness estimation than heterogeneity in the spatial distribution of species. Synthesis and applications. Our analysis demonstrates significant differences in the accuracy of different methods for estimating total species richness from a limited sample. Our work highlights the importance of knowing what an estimator is estimating so that when we characterize or compare sites, whether in ecological or conservation research, we do so with explicit knowledge of the potential biases in our chosen methodology.