Applications in adaptive cluster sampling of Gulf of Alaska

In nature, populations are sometimes distributed in a patchy, rare, or aggre-gated manner. Conventional sampling designs such as simple random sampling (SRS) do not take advantage of this spatial differentiation. Thompson (1990) introduced a sampling design called adaptive cluster sampling (ACS) to survey these types of distributions. Adaptive cluster sampling, in theory, can be much more precise for a given amount of effort than conventional sampling designs (Thompson, 1990). In practice, however, this is not always the case. In some cases, the variance is greatly reduced, but bias is induced from stopping rules and criterion values that are sometimes changed mid-survey (Lo et al., 1997). In 1998, we conducted a survey on Gulf of Alaska rockfi sh in which ACS was effi cient and successful, but the gains in precision, if any, were small compared to those of a SRS of the same size (Quinn et al., 1999; Hansel-man et al., 2001). Recently papers about ACS have included effi ciency comparisons (Christ-work has been done on determining the criterion value that, when exceeded, Abstract—Adaptive cluster sampling Abstract—Adaptive cluster sampling Abstract— (ACS) has been the subject of many publications about sampling aggregated populations. Choosing the criterion value that invokes ACS remains problematic. We address this problem using data from a June 1999 ACS survey for rockfish, specifically for Pacific ocean perch (Sebastes alutus), and for shortraker (S. borealis) and rougheye (S. aleutianus) rockfi sh combined. Our hypotheses were that ACS would out-perform simple random sampling (SRS) for S. alutus and would be more applicable for S. alutus than for S. borealis and S. aleutianus combined because populations of S. alutus are thought to be more aggregated. Three alternatives for choosing a criterion value were investigated. We chose the strategy that yielded the lowest criterion value and simulated the higher criterion values with the data after the survey. Systematic random sampling was conducted across the whole area to determine the lowest criterion value, and then a new systematic random sample was taken with adaptive sampling around each tow that exceeded the fi xed criterion value. ACS yielded gains in precision (SE) over SRS. Bootstrapping showed that the distribution of an ACS estima-tor is approximately normal, whereas the SRS sampling distribution is skewed and bimodal. Simulation showed that a higher criterion value results in substantially less adaptive sampling with little tradeoff in precision. When time-effi ciency was examined , ACS quickly added more …