Application of a theoretical simulator to the optimisation of risk-based invasive species surveillance - Thomas I. Gibson; Rebecca S. Millard; Isla MacMillan; Nick Taylor; Mark Thrush; Hannah Tidbury (NeoBiota)
Early detection and rapid response are critical to the successful management of non-indigenous species (NIS) and rely on effective surveillance programmes. Risk-based surveillance, where surveillance targets high risk locations, is the most efficient form of NIS surveillance. However, further research is required on the impact of different levels of emphasis on risk, in sampling designs and on surveillance efficacy. This study implements a theoretical surveillance simulator to model the relative merit of different surveillance strategies with different levels of focus on NIS risk for NIS detection at one or more sites. Three potential surveillance scenarios were modelled: random, risk-based and heavy risk-based surveillance, each with three distributions of combined NIS risks of introduction and establishment: exponential, random and uniform. An example analysis using model derived NIS risk data is also provided. Sensitivity and elasticity analyses were conducted to identify variables which influence model outputs. The interaction between sampling method detection probability and changes in NIS abundance was modelled. It was found that NIS risk distribution influences the relative performance of different surveillance strategies and that risk- and heavy risk-based surveillance have lower times to detections and, generally, higher surveillance probabilities of detection compared to random surveillance at more skewed NIS risk distributions. However, there was a trade-off between short detection time and detection failure in risk-based and particularly heavy risk-based surveillance. Therefore, an over-emphasis on risk-based surveillance could provide suboptimal NIS detection. Sensitivity and elasticity analysis showed that the number of NIS seed sites, mean site visit rate and method detection probability had the largest effects on detection time, highlighting the complexity of designing surveillance programmes. In conclusion, the optimal surveillance strategy is conditional on the risk distribution and this study highlights the value of model-based simulators to guide decision-making in the design of NIS surveillance programmes.