OPTIMIZATION OF VARIABLE AREA TRANSECT SAMPLING USING MONTE-CARLO SIMULATION

An extensive simulation study was conducted to optimize the number, r, of population members to be encountered from each random starting poi nt in variable area transect (VAT) sampling. The quality of estimation provided by the original calculation formula presented by K. R. Parke r in 1979 was compared to another formula that was a Morisita analog i ntended to reduce bias when sampling aggregated populations. Monte Car lo simulations covered 64 combinations of four spatial patterns, four sample sizes, and four densities. Values of r from 3 through 10 were c onsidered in each case. Relative root mean squared error was used as t he primary assessment criterion. Superior estimation properties were f ound for r > 3, but diminishing returns, relative to the potential for increased effort in the field, were found for r > 6. The original est imation formula consistently provided results that were superior to th e Morisita analog, with the difference most pronounced in the aggregat e patterns for which the Morisita analog was intended. As long as the sampled populations displayed randomness in location of individuals, r ather than systematic patterns that are uncommon in nature, the varian ce formula associated with the original estimation formula performed w ell. Additional simulations were conducted to examine four confidence interval methods for potential use in association with the Parker orig inal estimation method. These simulations considered only the sample s izes for which the best estimation was achieved in the earlier simulat ions. The confidence interval method developed by Parker worked well f or 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 ge neral use.