The use of adaptive cluster sampling for hydroacoustic surveys

Abstract

Resource managers are often required to estimate the size of a wildlife population based on sampling surveys. This problem is especially critical in fisheries, where stock-size estimation forms the basis for key policy decisions. This study looks at design-based methods for a hydroacoustic fisheries survey, with the goal of improving estimation when the target stock has a patchy spatial distribution. In particular, we examine the efficiency and feasibility of a relatively new design-based method known as adaptive cluster sampling (ACS). A simulation experiment looks at the relative efficiency of ACS and traditional sampling designs in a hydroacoustic survey setting. Fish densities with known spatial covariance are generated and subjected to repeated sampling. The distributions of the different estimators are compared.Hydroacoustic data frequently display strong serial correlation along transects and so traditional designs based on one-stage cluster sampling are appropriate. Estimates of total stock size for these designs had a markedly skewed distribution. ACS designs performed better than traditional designs for all stocks with small-scale spatial correlation in fish density, yielding estimates with lower variance. ACS estimators were not skewed and had a lower frequency of large errors. For the most variable stock the use of ACS reduced the coefficient of variation (CV) of the stock size estimate from over 0.9 to around 0.5. Differences between traditional and ACS designs were consistent over multiple realizations of each spatial covariance model.A survey of rainbow smelt (Osmerus mordax) in the eastern basin of Lake Erie was used as a case study for development of a survey design. A field trial showed that use of ACS for the survey is feasible but pointed out some areas for further research. The biggest drawback to use of ACS is uncertainty in the final sample size. This can be partially controlled by applying ACS within a stratified design. ACS retains the unbiased and non-parametric properties of design-based estimation but allows increased sampling in high-density areas that are of greater biological interest. For stocks with an aggregated or patchy spatial distribution ACS can provide a more precise estimate of stock size than traditional survey methods.

Paddlefish as Potential Acoustic Targets for Abundance Estimates

“…Conners and Schwager (2002) have shown that a simple sample mean may have a strong nonnormal distribution when transects are sampled from a spatially clustered population. If the distribution of paddlefish or other large fishes is clustered, then results such as these may violate the standard design assumption that target organisms are evenly distributed in space such that each transect may be considered a random draw from an identical underlying distribution.…”

Status of pelagic prey fishes in Lake Michigan, 2013

“…Cluster sampling techniques are appropriate for acoustic data, which represent a continuous stream of autocorrelated data (Williamson 1982;Connors and Schwager 2002). Density and biomass values for each ESDU in each stratum were weighted by dividing the stratum area (measured using GIS) by the number of ESDUs in the stratum.…”

Adaptive survey designs for sampling rare and clustered populations

“…Some recent examples are the use of adaptive cluster sampling for surveys of plants [25], waterfowl [31], seaweed [13]), shellfish [33], marsupials [30], forests [35,18], herpetofauna [23], larval sea lampreys [34]), sediment load in rivers [2]), in hydroacoustic surveys [10] and fish eggs [16].…”