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Adaptive Cluster Sampling (ACS) is introduced as a technique to use when"natural" groupings are evident in a spatially distributed population,especially sparsely distributed populations. An ACS sampling design will allowefficient allocation of survey manpower with more effective decision rules forwhere/when to allocate those resources. In particular, given a clustereddistribution, ACS would result in a lower variance of the mean estimator thanSimple Random Sampling (SRS). This paper derives a linear inequality based on aratio of variances and the SRS sample size to determine the conditions underwhich ACS would be a more appropriate sampling strategy. This inequality couldbe used to make preliminary decisions on the potential benefits of pursuing anACS design over an SRS design. The relationship between relative efficiency ofACS over SRS is discussed for (1) variable conditions for neighborhoodexpansion (Conditions to Adpat) (2) the degree of clustering and (3) thepercent of hits in a particular neighborhood. Simulation results demonstratehow 'rare' clusters should be for ACS to be a better design than SRS. Therelationships that emerge from the simulation provide insights that were notapparent in the analysis. The results are important in the estimation ofthresher shark landings along the California coast.

Estimation of relative efficiency of adaptive cluster vs traditional sampling designs applied to arrival of sharks