Choosing the best non-parametric richness estimator for benthic macroinvertebrates databases

摘要: Non-parametric estimators allow to compare the estimates of richness among data sets from heterogeneous sources. However, since estimator performance depends on species-abundance distribution sample, preference for one or another is a difficult issue. The present study recovers and revalues some criteria already in literature order choose most suitable streams macroinvertebrates, provides tools apply them. Two abundance four incidence were applied regional database at family genus level. They evaluated under criteria: sub-sample size required estimate observed richness; constancy size; lack erratic behavior similarity curve shape through different sets. Among estimators, Jack1 had best performance. Between ACE was when small Chao1 when the high. uniformity curves shapes allowed describe general sequences that could act as references estimations databases infer possible (i.e expected richness) if sample larger. These results can be very useful environmental management, update state knowledge macroinvertebrates.