Testing species richness estimation methods using museum label data on the Danish Asilidae

Museum collections are treasure troves of biodiversity information thatcan potentially be used for species richness estimation. Using label data on theDanish Asilidae (Diptera), we test eight species richness estimation techniques(abundance-based coverage estimator (ACE), ICE, Chao1, Chao2, first and secondorder Jackknife, Bootstrap and MMMeans) by comparing the estimates to the numberof species likely to occur in Denmark based on distributional information,expert opinion, and a species–area curve. We are investigating which ofthe estimators are most suited for the task. Furthermore, through theuse of four different subsampling schemes we study which kind of label information isnecessary in order to apply these estimation procedures. The first and secondorder Jackknife estimators yield the most accurate estimate of the number ofcollectable species in Denmark, while ACE, Bootstrap and Chao1 only provideslight improvements over observed values. We find that all estimatorsunderestimate the true diversity of Danish Asilidae and speculate that thisperformance is due to a discrepancy between the total and the collectable faunain the region. Finally, we discuss the implications for species richnessestimation and emphasize that for most terrestrial arthropod taxa thesediscrepancies are of such a magnitude that estimated species richness values maybe dangerously low and of limited use in conservation decision making.