David J. Huggard

A linear programming model of herbivore foraging: imprecise, yet successful?

I assessed the effects of the sampling error of input variables on the energy-maximizing diets of 14 grassland herbivores that Belovsky (1986) predicted using a linear programming model of optimal foraging. Monte Carlo simulations showed that the error reported in the estimates of the variables generated wide confidence intervals on predicted diets of the species. Given this imprecision in the predictions, the predicted diets that Belovsky reported were unexpectedly similar to the observed diets. The high correlation between predicted and observed diets reported by Belovsky was only attained in 0.01% of the simulation runs. Simulations assuming a variety of relationships between the sampling error in the different variables did not alter this conclusion. Incorporating the sampling error in even a single variable causes wide variability in the predicted diets. This analysis suggests that the high levels of accuracy reported for the linear programming approach will be difficult to repeat.