Using ecological community data to optimise the sensitivity and specificity of biosecurity surveillance networks

Trade is never zero risk, despite our best efforts there is always some residual probability that an unwanted, potentially invasive, species will cross our border. The most crucial element that prevents an incursion from progressing to an establishment is early detection, thus maximising potential eradication success. Detection relies on surveillance and Biosecurity NZ operates 13 targeted surveillance programmes (particular species or pathways) and a general surveillance programme that encourages public reporting via a 0800 hot line. New Zealand does not operate a non-specific trapping programme at first ports of entry, e.g., flight intercept traps at air and sea ports. However, internationally such trapping programmes have shown that they can detect new incursions. Globally the big research gap for non-specific trapping is that we do not understand the sensitivity and specificity of a given trapping programme design. We propose to solve this problem by using ecological community data of invertebrates collected by a range of different trap types, e.g., pitfall, flight intercept etc. Normally when analysing such data for ecological purposes we remove rare species from the analysis. However, these are the species that we want to detect for biosecurity purposes and thus surveillance network must be optimised to detect these rare events.

Supervised by Dr Steve Pawson (SoF), Prof Michael Plank and Blair Robertson (Maths and Statistics) with advisors from Scion (Dr Rebecca Turner) and CEBRA (Prof Andrew Robinson) this PhD is part of the newly funded transdisciplinary UC Biosecurity Innovations (UCBI) research cluster. Applicants will be mentored by UCBI to incorporate multiple knowledge domains into their research plan. The applicant will collate and curate an international dataset of community ecology studies with which to test mathematical constructs that explain the processes by which rare species accumulate in samples. A practical field-based component to test specific hypotheses can be included and tailored to the interests of the applicant.

The ideal candidate will have an interest in biological systems and a Bachelor and Masters level qualification in mathematics, statistics or closely related subject, with prior learning in areas such as mathematical modelling, stochastic processes, and sampling design. Biologists that have a strong affinity for, and proven experience in, applied mathematical problems and are wanting to expand on this as a PhD may be considered.