Optimization of Variable Area Transect Sampling Using Monte Carlo Simulation

Abstract

An extensive simulation study was conducted to optimize the number, r, of population members to be encountered from each random starting point in variable area transect (VAT) sampling. The quality of estimation provided by the original calculation formula presented by K. R. Parker in 1979 was compared to another formula that was a Morisita analog intended to reduce bias when sampling aggregated populations. Monte Carlo simulations covered 64 combinations of four spatial patterns, four sample sizes, and four densities. Values of r from 3 through 10 were considered in each case. Relative root mean squared error was used as the primary assessment criterion. Superior estimation properties were found for r > 3, but diminishing returns, relative to the potential for increased effort in the field, were found for r > 6. The original estimation formula consistently provided results that were superior to the Morisita analog, with the difference most pronounced in the aggregate patterns for which the Morisita analog was intended. As long as the sampled populations displayed randomness in location of individuals, rather than systematic patterns that are uncommon in nature, the variance formula associated with the original estimation formula performed well. Additional simulations were conducted to examine four confidence interval methods for potential use in association with the Parker original estimation method. These simulations considered only the sample sizes for which the best estimation was achieved in the earlier simulations. The confidence interval method developed by Parker worked well for populations with random spatial patterns, but it rarely achieved 80% (generally much less) of target coverage for populations displaying aggregation. A nonparametric confidence interval method presented here, or a combination of it with the Parker method, is recommended for general use.