Estimating abundance of spatially aggregated populations: comparing adaptive sampling with other survey designs

摘要

The main goal in estimating population abundance is to maximize its accuracy and precision. This is difficult when the survey area is large and resources are limited. We implemented a feasible adaptive sampling survey applied to an aggregated population in a marine environment and compared its performance with five classical survey designs. Specifically, larval walleye pollock (Theragra chalcogramma) in the Gulf of Alaska was used as an example of a widespread aggregated population. The six sampling designs included (i) adaptive cluster, (ii) simple random, (iii) systematic, (iv) systematic cluster, (v) stratified systematic, and (vi) unequal probability. Of the five different adaptive estimators used for the adaptive cluster design, the modified Hansen-Hurwitz performed best overall. Of the six survey designs, the stratified systematic survey provided the best overall estimator, given there was accurate prior information on which to base the strata. If no prior information was available, a systematic survey was best. A systematic survey using a single random starting point with a simple random estimator performed as well as and sometimes better than a systematic cluster survey with two starting points (clusters). The adaptive cluster survey showed no advantages when compared with these two designs and furthermore presented substantial logistical challenges.