Correcting for spatial autocorrelation in sequential sampling

Abstract

1. Sequential sampling is attractive because it permits the user to choose, and efficiently achieve, desired confidence interval lengths. Sequential sampling has been broadly applied in the inventory of ecological resources. 2. Using case studies and simulations, we demonstrate that estimates of the population mean that are derived from sequential sampling can be overconfident if the population is autocorrelated, that is, that the confidence intervals are too short. We test a model-based correction designed to ameliorate this effect of autocorrelation upon the estimates of confidence intervals from sequential sampling. 3. The correction is useful in realistic situations. Among the scenarios ..