TRAWL SURVEY ESTIMATION USING A COMPARATIVE APPROACH BASED ON LOGNORMAL THEORY

The spatial distributions of marine biota are frequently patchy, Sampl es taken from these populations are characterized by values which are mostly small, relative to the population mean, and a few that are very large. It is therefore difficult to estimate stock size using convent ional methods. We performed Monte Carlo simulations based on trawl dat a for Dungeness crab Cancer magister and compared the behavior of thre e estimators of central tendency: sample mean, geometric mean, and a l ognormal estimate. Although the sample mean is unbiased, results indic ate that single estimates of the population mean (and thus population estimates obtained using area-swept) may be overly sensitive to extrem e values; confidence intervals are large and capture the true value at a level well below that prescribed. Estimates of the geometric mean e xhibit more stable behavior about its parameter, with mixed results fo r the lognormal estimate. We propose a conservative approach based on comparison of trends found in each of the three estimators. Moreover, we suggest that abundance of aggregated stocks should be indexed with an estimator that has more desirable statistical properties, such as t he geometric mean. This may reduce error associated with conventional fisheries stock-assessment practices and thus provide for more effecti ve management of overdispersed stocks.