Requirements, design and implementation of a general model of biological invasion

David Savage, Michael Renton

    Research output: Contribution to journalArticle

    15 Citations (Scopus)

    Abstract

    The speed at which a response to a novel biological invasion can be developed and implemented plays a crucial role in the ability of biosecurity practitioners to successfully contain or eradicate the invading organism. In developing a response to a novel invasion, computational models of biological spread can play a key role, allowing practitioners to rapidly evaluate a range of invasion scenarios and the likely distribution of the invading population over time. This in turn can allow practitioners to compare different response plans and select those that will be most cost-effective and most likely to succeed. Unfortunately, the development of models that are capable of providing a realistic description of invasive spread is a costly and time consuming exercise and developing models specifically tailored to each of the vast array of potentially invasive organisms is infeasible. Therefore, we have developed a general model of biological invasion (GMBI) that is capable of simulating the invasive spread of a diverse range of organisms across heterogeneous landscapes, and can be used to represent particular invasion scenarios. The GMBI includes a small, highly biologically meaningful parameter set that can be relatively easily estimated using expert knowledge, and can therefore be quickly setup to simulate the spread of organisms which have not previously been well characterised. In this paper we discuss the desirability of a GMBI and elucidate the characteristics that are required. We then describe the formulation of a model that meets these requirements and demonstrate how it meets these requirements by parameterising the model to simulate the spread of two very different types of invasive organisms, namely a fungal pathogen and a pest beetle. These simulations demonstrate the flexibility of our GMBI, and the ease with which the model can be parameterised using parameter values found in the literature or obtained through expert elicitation. © 2013 Elsevier B.V.
    Original languageEnglish
    Pages (from-to)394-409
    JournalEcological Modelling
    Volume272
    DOIs
    Publication statusPublished - 2014

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