In most real-world contexts the sampling effort needed to attain
an accurate estimate of total species richness is excessive.
Therefore, methods to estimate total species richness from incomplete
collections need to be developed and tested. Using
real and computer-simulated parasite data sets, the performances of 9 species
richness estimation methods were compared.
For all data sets, each estimation method was used to calculate the
projected species richness at increasing levels of sampling
effort. The performance of each method was evaluated by calculating the
bias and precision of its estimates against the
known total species richness. Performance was evaluated with increasing
sampling effort and across different model
communities. For the real data sets, the Chao2 and first-order jackknife
estimators performed best. For the simulated data
sets, the first-order jackknife estimator performed best at low sampling
effort but, with increasing sampling effort, the
bootstrap estimator outperformed all other estimators. Estimator
performance increased with increasing species richness,
aggregation level of individuals among samples and overall population
size. Overall, the Chao2 and the first-order jackknife
estimation methods performed best and should be used to control for the
confounding effects of sampling effort in studies
of parasite species richness. Potential uses of and practical problems
with species richness estimation methods are discussed.