  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 allow
efficient allocation of survey manpower with more effective decision rules for
where/when to allocate those resources. In particular, given a clustered
distribution, ACS would result in a lower variance of the mean estimator than
Simple Random Sampling (SRS). This paper derives a linear inequality based on a
ratio of variances and the SRS sample size to determine the conditions under
which ACS would be a more appropriate sampling strategy. This inequality could
be used to make preliminary decisions on the potential benefits of pursuing an
ACS design over an SRS design. The relationship between relative efficiency of
ACS over SRS is discussed for (1) variable conditions for neighborhood
expansion (Conditions to Adpat) (2) the degree of clustering and (3) the
percent of hits in a particular neighborhood. Simulation results demonstrate
how 'rare' clusters should be for ACS to be a better design than SRS. The
relationships that emerge from the simulation provide insights that were not
apparent in the analysis. The results are important in the estimation of
thresher shark landings along the California coast.
