Embedding Population Dynamics Models in Inference

TL;DR

This paper presents a Bayesian framework for constructing and estimating discrete-time population models from time series data, aiding sustainable management of endangered species with quantified uncertainties.

Contribution

It introduces a simple building block approach for modeling populations and demonstrates how to estimate parameters and uncertainties using Bayesian methods.

Findings

Effective population predictions with uncertainty quantification.

Application to British grey seal population.

Highlights advantages and pitfalls of the approach.

Abstract

Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple ``building block'' approach to formulating discrete-time models. We show how to estimate the parameters of such models from time series of data, and how to quantify uncertainty in those estimates and in numbers of individuals of different types in populations, using computer-intensive Bayesian methods. We also discuss advantages and pitfalls of the approach, and give an example using the British grey seal population.