Species richness is a widely used surrogate for the more complex concept of
biological diversity. Because species richness is often central to ecologi
cal study and the establishment of conservation priorities, the biases and
merits of richness measurements demand evaluation. The jackknife and bootst
rap estimators can be used to compensate for the underestimation associated
with simple richness estimation (or the sum of species courted in a sample
). Using data from five forest communities, we analyzed the simple measure
of richness, the first- and second-order jackknife, and the bootstrap estim
ators with simulation and resampling methods to examine the effects of samp
le size on estimator performance. Performance parameters examined were syst
ematic under- or overestimation (bias), ability to estimate consistently (p
recision), and ability to estimate true species richness (accuracy).
For small sample sizes in all studied communities (less than similar to 25%
of the total community), the least biased estimator was the second-order j
ackknife, followed by the first-order jackknife, the bootstrap, and the sim
ple richness estimator. However, with increases in sample size, the second-
order jackknife, followed by the first-order jackknife and the bootstrap, b
ecame positively biased. The simple richness estimator was the most precise
estimator in all studied communities, but it yielded the largest underesti
mate of species richness at all sample sizes. The relative precision of the
four estimators did not differ across communities, but the magnitude of es
timator variance is dependent on the sampled community. Differences in accu
racy among the estimators were not independent of community, and accuracy p
atterns were associated with community species diversity. The results of th
is study can assist policy makers, researchers, and managers in the selecti
on of appropriate sample sizes and estimators for richness estimation and s
hould facilitate the ongoing assessment of local, and ultimately global, bi
odiversity.