Data from: A spatial kernel density method to estimate diet composition of fish

We present a novel spatially-explicit kernel density approach to estimate
the proportional contribution of a prey to a predator’s diet by weight.
First, we compare the spatial estimator to a traditional cluster-based
approach using a Monte Carlo simulation study. Next we compare the diet
composition of three predators from Pamlico Sound, North Carolina to
evaluate how ignoring spatial correlation affected diet estimates. The
spatial estimator had lower MSE values compared to the traditional
cluster-based estimator for all Monte Carlo simulations. Incorporating
spatial correlation when estimating the predator’s diet resulted in a
consistent increase in precision across multiple levels of spatial
correlation. Bias was often similar between the two estimators but when it
differed it mostly favored the spatial estimator. The two estimators
produced different estimates of proportional contribution of prey to the
diets of the three field-collected predator species, especially when
spatial correlation was strong and prey were consumed in patchy areas. Our
simulation and empirical data provide strong evidence food habits data
should be modeled using spatial approaches and not treated as
spatially-independent.