Correcting for spatial autocorrelation in sequential sampling.
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. 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. The correction is useful in realistic situations. Among the scenarios we tested, better confidence interval coverage was achieved with larger sample sizes, and coverage rates were poor at smaller sample sizes. Nominal coverage could be attained even when the wrong model was used, although only at the cost of requiring a much higher average sample size. Synthesis and applications. If sequential sampling is naively applied in a population that has autocorrelation, then confidence intervals for population parameters will be too short, and the usefulness of the sample will be overestimated. We recommend using a correction to lengthen the estimated confidence intervals. Our results suggest that this correction requires a substantial sample size, up to several hundred units, in order to provide nominal coverage. Sequential sampling seems risky in autocorrelated populations if the realized sample size is small.