Assessing Uncertainty in Coastal Marsh Core Sampling for Waterfowl Foods

Quantifying foraging resources available to waterfowl in different habitat types is important for estimating energetic carrying capacity. To accomplish this, most studies collect soil-core samples from the marsh substrate, sieve and sort food items, and extrapolate energy values to wetland or landscape scales. This is a costly and time-intensive process; furthermore, extrapolation methods yield energy estimates with large variances relative to the mean. From both research and management perspectives, it is important to understand sources of this variation and estimate the number of soil cores needed to reduce the variance to desired levels. Using 2,341 cores collected from freshwater and salt marsh habitats at four sites along the Atlantic Coast, we examined sampling variation and biological variation among sites and habitats. When we removed extreme outliers in the data caused by large animal food items found in a small core sample, estimates of energy density decreased by an order of magnitude for most habitats. After removing outliers, we found inconsistent geographical variation among habitat types that was especially pronounced in freshwater and no evidence for within-season temporal depletion of food resources for any site or habitat. We used a Monte Carlo simulation approach to estimate the optimal number of cores (minimizing both cost and estimated variance) sampled in each habitat type. Across most contexts, a reduction in the coefficient of variation reached diminishing returns near 40 core samples. We recommend that researchers explicitly address outliers in the data and managers acknowledge the imprecision that can arise from including or excluding outliers when estimating energy density at landscape scales. Our results suggest that collecting 40–50 cores per habitat type was sufficient to reduce the variance to acceptable levels while minimizing overall sampling costs.