Adaptive sampling

The abundance of rare species of plants and animals may often prove difficult to estimate due to the isolated patchy distribution of individuals. Adaptive sampling may prove more effective than other sampling strategies for such species. In adaptive cluster sampling an initial SRS of population units is selected. Further adaptive sampling in the neighborhood of these units is then carried out whenever the value of y in a selected unit meets or exceeds a criterion value, c, which may often be just a single individual. This sampling procedure can be shown to lead to selection of clusters of units for which, with the exception of edge units, all units in the selected clusters have y≥c. If the initial sample is large enough to encounter some isolated patches of individuals, this approach may outperform SRS with mean-per-unit estimation. Drawbacks of this approach include the facts that the eventual number of population units which will need to be measured is random and unknown prior to execution of the survey, and it is difficult to specify the magnitude of the adaptive sampling criterion, c. Therefore, the total cost and time needed to complete an adaptive sampling survey can be highly unpredictable. Nevertheless, the theory is intriguing and has obvious intuitive appeal. Once a very rare individual has been encountered, it makes good sense to search very carefully in the neighborhood of the location where that rare individual has been found.