  Understanding functional response within a predator-prey dynamic is a
cornerstone for many quantitative ecological studies. Over the past 60 years,
the methodology for modelling functional response has gradually transitioned
from the classic mechanistic models to more statistically oriented models. To
obtain inferences on these statistical models, a substantial number of
experiments need to be conducted. The obvious disadvantages of collecting this
volume of data include cost, time and the sacrificing of animals. Therefore,
optimally designed experiments are useful as they may reduce the total number
of experimental runs required to attain the same statistical results. In this
paper, we develop the first sequential experimental design method for
predator-prey functional response experiments. To make inferences on the
parameters in each of the statistical models we consider, we use sequential
Monte Carlo, which is computationally efficient and facilitates convenient
estimation of important utility functions. It provides coverage of experimental
goals including parameter estimation, model discrimination as well as a
combination of these. The results of our simulation study illustrate that for
predator-prey functional response experiments sequential design outperforms
static design for our experimental goals. R code for implementing the
methodology is available via
https://github.com/haydenmoffat/sequential_design_for_predator_prey_experiments.
