Changes in the spatial distribution of subtidal macrobenthos due to predation by white shrimp (<Emphasis Type="Italic">Litopenaeus setiferus</Emphasis>)

Manipulative caging experiments were conducted in North Inlet, South Carolina, to measure the predatory effect of juvenile penaeid white shrimp,Litopenaeus setiferus, on their subtidal macrobenthic prey. We used the natural neighbor interpolation procedure within a Geographic Information System (GIS) to map macrobenthos distributions at both the start and end of the cage deployments. Moran’s I, a commonly used index of spatial autocorrelation, provided a quantitative metric for evaluating the statistical significance of the observed changes. We tested the hypothesis that juvenile white shrimp are optimal foragers by assessing whether their predatory behavior was targeted at higher density macrobenthos patches inside the enclosures, resulting in a more homogeneous distribution of prey after seven days. Since large changes in patchiness could occur over seven days without incurring a significant change in index value, we treated each index as a continuous measure of patchiness, and examined whether the value increased or decreased consistently among treatment replicates. Using Moran’s I, the abundance and spatial distribution of macrobenthos inside control, partial, open, and shrimp inclusion treatments varied in their response. After seven days, decreased patchiness was consistently observed in the high density shrimp treatment replicates, and increased patchiness in the open plots. The GIS natural neighbor interpolation created a succinct visual representation of dramatic changes in prey spatial distribution and prey densities throughout each cage. The GIS interpolation conveyed the dynamic nature of the spatial variability that would not have been evident by calculation of Moran’s I alone. Although we could only weakly support our hypothesis, the combination of visual interpolation methods with index calculations has great potential for gaining further insights into the role of different factors as they affect changes in spatial distribution of benthic infauna.