Optimizing performance of nonparametric species richness estimators under constrained sampling.

ABSTRACT: Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species-abundance data from a single sample into two equally sized samples, and using an appropriate incidence-based estimator to estimate richness. To test this method, we assume a lognormal species-abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulation

SUBMITTER: Rajakaruna H 

PROVIDER: S-EPMC5513256 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature