Estimation of regional richness in marine benthic communities: quantifying the error
Limnol. Oceanogr. Methods 6:580-590 (2008) | DOI: 10.4319/lom.2008.6.580
ABSTRACT: Species richness is the most widely used measure of biodiversity. It is considered crucial for testing numerous ecological theories. While local species richness is easily determined by sampling, the quantification of regional richness relies on more or less complete species inventories, expert estimation, or mathematical extrapolation from a number of replicated local samplings. However the accuracy of such extrapolations is rarely known. In this study, we compare the common estimators MM (Michaelis-Menten), Chao1, Chao2, ACE (Abundance-based Coverage Estimator), and the first and second order Jackknifes against the asymptote of the species accumulation curve, which we use as an estimate of true regional richness. Subsequently, we quantified the role of sample size, i.e., number of replicates, for precision, accuracy, and bias of the estimation. These replicates were sub-sets of three large data sets of benthic assemblages from the NE Atlantic: (i) soft-bottom sediment communities in the Western Baltic (n = 70); (ii) hard-bottom communities from emergent rock on the Island of Helgoland, North Sea (n = 52), and (iii) hard-bottom assemblages grown on artificial substrata in Madeira Island, Portugal (n = 56). For all community types, Jack2 showed a better performance in terms of bias and accuracy while MM exhibited the highest precision. However, in virtually all cases and across all sampling efforts, the estimators underestimated the regional species richness, regardless of habitat type, or selected estimator. Generally, the amount of underestimation decreased with sampling effort. A logarithmic function was applied to quantify the bias caused by low replication using the best estimator, Jack2. The bias was more obvious in the soft-bottom environment, followed by the natural hard-bottom and the artificial hard-bottom habitats, respectively. If a weaker estimator in terms of performance is chosen for this quantification, more replicates are required to obtain a reliable estimation of regional richness.