Inferring fruit infestation prevalence from a combination of pre-harvest monitoring and consignment sampling data.

International trade in horticultural produce happens under phytosanitary inspection and production protocols. Fruit inspection typically involves the sampling and inspection of either 600-pieces or 2% of packed product within a single consignment destined for export, with the purpose of certification (typically with 95% confidence) that the true infestation level within the consignment in question doesn't exceed a pre-specified design prevalence. Sampling of multiple consignments from multiple production blocks in conjunction with pre-harvest monitoring for pests can be used to provide additional inference on the prevalence of infested fruit within an overall production system subject to similar protocols. Here we develop a hierarchical Bayesian model that combines in-field monitoring data with consignment sample inspection data to infer the prevalence of infested fruit in a production system. The results illustrate how infestation prevalence is influenced by the number of consignments inspected, the detection efficacy of consignment sampling, and in-field monitoring effort and sensitivity. Uncertainty in inspection performance, monitoring methods, and exposure of fruit to pests is accommodated using statistical priors within a Bayesian modelling framework. We demonstrate that pre-harvest surveillance with a sufficient density of traps and moderate detection sensitivity can provide 95% belief that the prevalence of infestation is below 1 × 10 - 6 . In the absence of pre-harvest monitoring, it is still possible to gain high confidence in a very low prevalence of infestation ( < 1 × 10 - 5 ) on the basis of multiple clean samples if the inspection sensitivity during consignment sampling is high and sufficient consignments are inspected. Our work illustrates the cumulative power of in-field surveillance and consignment sampling to update estimates of infestation prevalence.

Simulation to investigate site-based monitoring of pest insect species for trade.

Pest insect surveillance using lures is widely used to support market access requirements for traded articles that are hosts or carriers of quarantine pests. Modeling has been used extensively to guide the design of surveillance to support pest free area claims but is less commonly applied to provide confidence in pest freedom or low pest prevalence within sites registered for trade. Site-based surveillance typically needs to detect pests that are already present in the site or that may be entering the site from surrounding areas. We assessed the ability of site-based surveillance strategies to detect pests originating from within or outside the registered site using a probabilistic trapping network simulation model with random-walk insect movement and biologically realistic parameters. For a given release size, time-dependent detection probability was primarily determined by trap density and lure attractiveness, whereas mean step size (daily dispersal) had limited effect. Results were robust to site shape and size. For pests already within the site, detection was most sensitive using regularly spaced traps. Perimeter traps performed best for detecting pests moving into the site, although the importance of trap arrangement decreased with time from release, and random trap placement performed relatively well compared to regularly spaced traps. High detection probabilities were achievable within 7 days using realistic values for lure attractiveness and trap density. These findings, together with the modeling approach, can guide the development of internationally agreed principles for designing site-based surveillance of lure-attractant pests that is calibrated against the risk of non-detection.

Global mismatch of policy and research on drivers of biodiversity loss.

The United Nations 2030 Agenda for Sustainable Development calls for urgent actions to reduce global biodiversity loss. Here, we synthesize >44,000 articles published in the past decade to assess the research focus on global drivers of loss. Relative research efforts on different drivers are not well aligned with their assessed impact, and multiple driver interactions are hardly considered. Research on drivers of biodiversity loss needs urgent realignment to match predicted severity and inform policy goals.