A spatial kernel density method to estimate the diet composition of fish

Abstract

We present a novel spatially explicit kernel density approach to estimate the proportional contribution of a prey to a predator’s diet by mass. First, we compared the spatial estimator to a traditional cluster-based approach using a Monte Carlo simulation study. Next, we compared the diet composition of three predators from Pamlico Sound, North Carolina, to evaluate how ignoring spatial correlation affects diet estimates. The spatial estimator had lower mean squared error values compared with 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; however, 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 that data on food habits should be modeled using spatial approaches and not treated as spatially independent.

Spatio‐temporal analyses of marine predator diets from data‐rich and data‐limited systems

“…In addition, sampling surveys that specifically target rare feeding events and juvenile predators are critically needed to fill in gaps in our understanding of fish diet patterns (Binion-Rock et al, 2018;Sagarese et al, 2016).…”

“…The spatially explicit kernel density method was found to be more precise than a simple cluster-based method (Binion-Rock et al, 2018). Yet, Binion-Rock et al (2018) noted that, for data from long-tailed distributions, the bandwidth (i.e. the width of the kernel) that defines the amount of spatial smoothing is difficult to adjust to avoid erroneous noise in the tails of the estimates.…”

“…This can mask important fine‐scale patterns (e.g. Buckley, Ortiz, Kotwicki, & Aydin, 2016; Glaser, 2010) and can lead to biases in estimating predation pressure at the population level (Binion‐Rock, Reich, & Buckel, 2018; Reum, Blanchard, Holsman, Aydin, & Punt, 2019). Advances in quantitative methods for analysing spatially explicit diet data can provide new and more accurate insights into trophic dynamics in marine ecosystems and may strengthen efforts towards ecosystem‐based fisheries management (EBFM; Patrick & Link, 2015).…”

“…This is despite the fact that trophic interactions can vary widely within a given marine region (Buckley et al, 2016; Glaser, 2010; Tarnecki et al, 2016). To address this issue, Binion‐Rock et al (2018) designed a spatially explicit kernel density method, which takes spatial structure (i.e. spatial autocorrelation) into account to determine diet proportions.…”

Importance of Sample Gear in the Identification of Trophic Guilds and Forage Species in a Large Lagoonal Estuary

“…For each year and gear type, the proportional contribution of prey by weight to predator diets was estimated using a spatially explicit kernel density estimator (described by Binion‐Rock et al. ) that adjusts for both intra‐ and intersite correlation. Briefly, predator diets were interpolated over a series of grid locations overlaying the sampling area.…”