title: Agent-based Bayesian approach to monitoring the progress of invasive species eradication programs.
abstract: Eradication of an invasive species can provide significant environmental, economic, and social benefits, but eradication programs often fail. Constant and careful monitoring improves the chance of success, but an invasion may seem to be in decline even when it is expanding in abundance or spatial extent. Determining whether an invasion is in decline is a challenging inference problem for two reasons. First, it is typically infeasible to regularly survey the entire infested region owing to high cost. Second, surveillance methods are imperfect and fail to detect some individuals. These two factors also make it difficult to determine why an eradication program is failing. Agent-based methods enable inferences to be made about the locations of undiscovered individuals over time to identify trends in invader abundance and spatial extent. We develop an agent-based Bayesian method and apply it to Australia's largest eradication program: the campaign to eradicate the red imported fire ant (Solenopsis invicta) from Brisbane. The invasion was deemed to be almost eradicated in 2004 but our analyses indicate that its geographic range continued to expand despite a sharp decline in number of nests. We also show that eradication would probably have been achieved with a relatively small increase in the area searched and treated. Our results demonstrate the importance of inferring temporal and spatial trends in ongoing invasions. The method can handle incomplete observations and takes into account the effects of human intervention. It has the potential to transform eradication practices.
authors.affiliation: School of Mathematical Sciences, Monash University, Clayton Campus, VIC 3800, Australia. jonathan.keith@monash.edu
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Keith
authors.forename: Daniel
authors.initials: D
authors.lastname: Spring
mesh_terms: D000818:Animals; D001000:Ants / Q000502:physiology*; D001499:Bayes Theorem; D003247:Conservation of Natural Resources / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods*; D007300:Insect Control / Q000379:methods*; D058865:Introduced Species / Q000706:statistics & numerical data*; D008954:Models, Biological; D011157:Population Dynamics; D011793:Queensland
keywords: Bayesian models; Markov chain Monte Carlo; spread models