A comparison of survey method efficiencies for estimating densities of zebra mussels (Dreissena polymorpha)

Abstract

Abundance surveys are commonly used to estimate plant or animal densities and frequently require estimating detection probabilities to account for imperfect detection. The estimation of detection probabilities requires additional measurements that take time, potentially reducing the efficiency of the survey when applied to high-density populations. We conducted quadrat, removal, and distance surveys of zebra mussels (Dreissena polymorpha) in three central Minnesota lakes and determined how much survey effort would be required to achieve a pre-specified level of precision for each abundance estimator, allowing us to directly compare survey design efficiencies across a range of conditions. We found that the required sampling effort needed to achieve our precision goal depended on both the survey design and population density. At low densities, survey designs that could cover large areas but with lower detection probabilities, such as distance surveys, were more efficient (i.e., required less sampling effort to achieve the same level of precision). However, at high densities, quadrat surveys, which tend to cover less area but with high detection rates, were more efficient. These results demonstrate that the best survey design is likely to be context-specific, requiring some prior knowledge of the underlying population density and the cost/time needed to collect additional information for estimating detection probabilities.

Keywords: Abundance estimation; Aquatic invasive species; Detection probability; Distance-removal survey; Quadrat survey; Removal survey; Underwater visual survey.

Figure 1. Location of three lakes surveyed in Minnesota during the summer of 2018.

Solid black circles within each lake indicate surveyed locations. Lake surface area (in km²) is reported within each lake polygon.

Figure 2. Illustration of a transect for each of the survey techniques used in this study.

The blue-shaded area indicates the area surveyed by the dive team. Horizontal lines in the distance survey indicate the distance measures used to estimate detection probabilities.

Figure 3. Boxplot indicating the amount of time spent surveying a transect for distance, removal, and quadrat surveys in three Central Minnesota lakes surveyed during the summer of 2018.

The lower and upper hinges denote the first and third quartiles, and the horizontal line denotes the median. Points indicate the individual data points.

Figure 4. Estimated probability of detection, P^, for removal and distance surveys in three Central Minnesota lakes surveyed during the summer of 2018; detection probabilities were assumed to be one for quadrat surveys.

Error bars denote two standard errors.

Figure 5. Density estimates (individuals per m²) for quadrat, removal, and distance surveys in three Central Minnesota lakes surveyed during the summer of 2018.

Error bars denote two standard errors.

Figure 6. The estimated number of transects needed to achieve a coefficient of variation (CV) of 0.1.

Surveys were conducted in three Central Minnesota lakes during the summer of 2018.