Performance of species richness estimators across assemblage types and survey parameters

• Published in: Global ecology and biogeography 2014-05, Vol.23 (5), p.585-594
• Main Authors: Reese, Gordon C, Wilson, Kenneth R, Flather, Curtis H
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Bias; Biodiversity; Biogeography; Biological and medical sciences; Chaos theory; community ecology; Conservation biology; Estimating techniques; Estimation bias; Estimators; Fundamental and applied biological sciences. Psychology; General aspects; nonparametric estimator; Population ecology; Population estimates; probability; sample coverage; selection framework; simulation; Species diversity; Survey design; surveys; Synecology; variance; Wildlife ecology
• ISSN: 1466-822X; 1466-8238; 1466-822X
• DOI: 10.1111/geb.12144

AIM: A raw count of the species encountered across surveys usually underestimates species richness. Statistical estimators are often less biased. Nonparametric estimators of species richness are widely considered the least biased, but no particular estimator has consistently performed best. This is partly a function of estimators responding differently to assemblage‐level factors and survey design parameters. Our objective was to evaluate the performance of raw counts and nonparametric estimators of species richness across various assemblages and with different survey designs. LOCATION: We used both simulated and published field data. METHODS: We evaluated the bias, precision and accuracy of raw counts and 13 nonparametric estimators using simulations that systematically varied assemblage characteristics (number of species, species abundance distribution, total number of individuals, spatial configuration of individuals and species detection probability), sampling effort and survey design. Results informed the development of an estimator selection framework that we evaluated with field data. RESULTS: When averaged across assemblages, most nonparametric estimators were less negatively biased than a raw count. Estimators based on the similarity of repeated subsets of surveys were most accurate and their accumulation curves appeared to reach asymptotes fastest. Number of species, species abundance distribution and effort had the largest effects on performance, ultimately by affecting the proportion of the species pool contained in a sample. Our estimator selection framework showed promising results when applied to field data. MAIN CONCLUSIONS: A raw count of the number of species in an area is far from the best estimate of true species richness. Nonparametric estimators are less biased. Newer largely unused, estimators perform better than more well known and longer established counterparts under certain conditions. Given that there is generally a trade‐off between bias and precision, we believe that estimator variance, which is often not reported when presenting species richness estimates, should always be included.