Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables.

Introduction pathway studies generally describe the diverse routes by which non-indigenous species (NIS) can be introduced but rarely consider multiple introduction pathways occurring simultaneously. In this study, multiple pathways of NIS introduction were investigated during an industrial development on a remote island off the Australian coast. Fifteen introduction pathways were categorized in association with importing locality and the type of cargo they transported. The number and types of detection events for each introduction pathway were recorded during biosecurity inspections, cargo clearances, and surveillance conducted between 2009 and 2015. In total, more than 600,000 biosecurity inspections were completed, with 5,328 border detection events recorded constituting less than 1% of the biosecurity inspections. The border inspection events were classified as animals, plant material, soil, and organic matter, with 60% identified as dead or non-viable and 40% as alive. Of those detections, 2153 were classified as NIS, consisting of 659 identified species. Live NIS detected at the border constituted only 2% of the detections. Cargo vessel and inward-bound passenger numbers peaked during the major construction period and were associated with an increase in the number of live NIS detections. All introduction pathways have complexities, unique structural aspects, and niche areas that supported NIS in surviving the effects of treatment and evading detection during the mandatory compliance inspection. This study highlights that biosecurity incursions can be minimized if a systems approach is adopted to complement traditional and other biosecurity surveillance measures.