Biological Invasions

, Volume 19, Issue 10, pp 2959–2970 | Cite as

Modelling tropical fire ant (Solenopsis geminata) dynamics and detection to inform an eradication project

  • Christopher M. Baker
  • Jarrod C. Hodgson
  • Elena Tartaglia
  • Rohan H. Clarke
Original Paper

Abstract

Invasive species threaten endangered species worldwide and substantial effort is focused on their control. Eradication projects require critical resource allocation decisions, as they affect both the likelihood of success and the overall cost. However, these complex decisions must often be made within data-poor environments. Here we develop a mathematical framework to assist in resource allocation for invasive species control projects and we apply it to the proposed eradication of the tropical fire ant (Solenopsis geminata) from the islands of Ashmore Reef in the Timor Sea. Our framework contains two models: a population model and a detection model. Our stochastic population model is used to predict ant abundance through time and allows us to estimate the probability of eradication. Using abundance predictions from the population model, we use the detection model to predict the probability of ant detection through time. These models inform key decisions throughout the project, which include deciding how many baiting events should take place, deciding whether to invest in detector dogs and setting surveillance effort to confirm eradication following control. We find that using a combination of insect growth regulator and toxins are required to achieve a high probability of eradication over 2 years, and we find that using two detector dogs may be more cost-effective than the use of lure deployment, provided that they are used across the life of the project. Our analysis lays a foundation for making decisions about control and detection throughout the project and provides specific advice about resource allocation.

Keywords

Ashmore Reef Eradication Invasive species Invertebrates Optimal detection Stochastic modelling Monitoring 

Supplementary material

10530_2017_1499_MOESM1_ESM.pdf (6.7 mb)
Supplementary material 1 (PDF 6872 kb)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Biological SciencesThe University of QueenslandBrisbaneAustralia
  2. 2.CSIRO Ecosystem Sciences, Ecosciences PrecinctBrisbaneAustralia
  3. 3.School of BiosciencesThe University of MelbourneMelbourneAustralia
  4. 4.School of Biological SciencesMonash UniversityMelbourneAustralia
  5. 5.School of Biological SciencesUniversity of AdelaideAdelaideAustralia
  6. 6.School of Mathematics and StatisticsThe University of MelbourneMelbourneAustralia

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