Approximate methods for dynamic ecological models

TL;DR

This paper discusses approximate Bayesian methods, especially Synthetic Likelihood, for ecological models, offering alternatives to particle filters and demonstrating their application on prey-predator population data.

Contribution

It introduces the use of Synthetic Likelihood as a practical alternative to particle filters in ecological state space models.

Findings

Synthetic Likelihood provides a viable alternative to particle filters.

Application to vole population dynamics demonstrates method effectiveness.

Highlights benefits of summary statistic-based inference in ecology.

Abstract

This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) by S. Sisson, L. Fan, and M. Beaumont. Here we describe some of the circumstances under which statistical ecologists might benefit from using methods that base statistical inference on a set of summary statistics, rather than on the full data. We focus particularly on one such approach, Synthetic Likelihood, and we show how this method represents an alternative to particle filters, for the purpose of fitting State Space Models of ecological interest. As an example application, we consider the prey-predator model of Turchin and Ellner (2000), and we use it to analyse the observed population dynamics of Fennoscandian voles.