Optimal Inspection of Imports to Prevent Invasive Pest Introduction

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.

Contaminated consignment simulation to support risk-based inspection design

Invasive nonnative plant pests can cause extensive environmental and economic damage and are very difficult to eradicate once established. Phytosanitary inspections that aim to prevent biological invasions by limiting movement of nonnative plant pests across borders are a critical component of the biosecurity continuum. Inspections can also provide valuable information about when and where plant pests are crossing national boundaries. However, only a limited portion of the massive volume of goods imported daily can be inspected, necessitating a highly targeted, risk-based strategy. Furthermore, since inspections must prioritize detection and efficiency, their outcomes generally cannot be used to make inferences about risk for cargo pathways as a whole. Phytosanitary agencies need better tools for quantifying pests going undetected and designing risk-based inspection strategies appropriate for changing operational conditions. In this research, we present PoPS (Pest or Pathogen Spread) Border, an open-source consignment inspection simulator for measuring inspection outcomes under various cargo contamination scenarios to support recommendations for inspection protocols and estimate pest slippage rates. We used the tool to estimate contamination rates of historical interception data, quantify tradeoffs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. These use cases demonstrate how this simulation approach permits testing inspection strategies and measuring quantities that would otherwise be impossible in a field-based setting. This work represents the first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends.

Invasive Species Policy Must Embrace a Changing Climate

With increasing impacts of climate change observed across ecosystems, there is an urgent need to consider climate change in all future environmental policy. But existing policy and management might be slow to respond to this challenge, leading to missed opportunities to incorporate climate change into practice. Furthermore, invasive species threats continue to rise and interact with climate change—exacerbating negative impacts. Enabling natural resource managers and individuals to be proactive about climate-driven invasive species threats creates a win–win for conservation. Recommendations include expanding opportunities for information sharing across borders, supporting proactive screening and regulation of high-risk species on the horizon, and incentivizing individual actions that reduce ecological impacts. In addition, invasive species risk should be considered when crafting climate mitigation and adaptation policy to reduce compounding stressors on ecosystems. As we develop much-needed tools to reduce harm, policy and management must consider the combined threats of invasions and climate change.

Estimating Consignment-Level Infestation Rates from the Proportion of Consignment that Failed Border Inspection: Possibilities and Limitations in the Presence of Overdispersed Data

Introduction of pests and diseases through trade is one of the main socioecological challenges worldwide. Targeted sampling at border security can efficiently provide information about biosecurity threats and also reduce pest entry risk. Prioritizing sampling effort requires knowing which pathways are most infested. However, border security inspection data are often right-censored, as inspection agencies often only report that a consignment has failed inspection (i.e., there was at least one unit infested), not how many infested units were found. A method has been proposed to estimate the mean infestation rate of a pathway from such right-censored data (Chen et al.). Using simulations and case study data from imported germplasm consignments inspected at the border, we show that the proposed method results in negatively biased estimates of the pathway infestation rate when the inspection data exhibit overdispersion (i.e., varying infestation rates among different consignments of the same pathway). The case study data also show that overdispersion is prevalent in real data sets. We demonstrate that the method proposed by Chen et al. recovers the median infestation rate of the pathway, rather than its mean. Therefore, it underpredicts the infestation rate when the data are overdispersed (in right-skewed distributions, the mean is above the median). To allow better monitoring and optimizing sampling effort at the border, we recommend that border protection agencies report all the data (the number of infested units found together with the sample size of the inspection) instead of only that the consignment failed inspection.

Optimal invasive species surveillance in the real world: practical advances from research

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

Estimating the benefit of quarantine: eradicating invasive cane toads from islands

Islands are increasingly used to protect endangered populations from the negative impacts of invasive species. Quarantine efforts on islands are likely to be undervalued in circumstances in which a failure incurs non-economic costs. One approach to ascribe monetary value to such efforts is by modeling the expense of restoring a system to its former state. Using field-based removal experiments on two different islands off northern Australia separated by &gt; 400 km, we estimate cane toad densities, detection probabilities, and the resulting effort needed to eradicate toads from an island. We use these estimates to conservatively evaluate the financial benefit of cane toad quarantine across offshore islands prioritized for conservation management by the Australian federal government. We calculate density as animals per km of freshwater shoreline, and find striking concordance of density estimates across our two island study sites: a mean density of 352 [289, 466] adult toads per kilometre on one island, and a density of 341 [298, 390] on the second. Detection probability differed between our two study islands (Horan Island: 0.1 [0.07, 0.13]; Indian Island: 0.27 [0.22, 0.33]). Using a removal model and the financial costs incurred during toad removal, we estimate that eradicating cane toads would, on average, cost between $22 487 [$14 691, $34 480] (based on Horan Island) and $39 724 [$22 069, $64 001] AUD (Indian Island) per km of available freshwater shoreline. We estimate the remaining value of toad quarantine across islands that have been prioritized for conservation benefit within the toads’ predicted range, and find the net value of quarantine efforts to be $43.4 [28.4–66.6] – $76.7 [42.6–123.6] M depending on which island dataset is used to calibrate the model. We conservatively estimate the potential value of a mainland cane toad containment strategy – to prevent the spread of toads into the Pilbara Bioregion – to be $80 [52.6–123.4] – $142 [79.0–229.0] M. We present a modeling framework that can be used to estimate the value of preventative management, via estimating the length and cost of an eradication program. Our analyses suggest that there is substantial economic value in cane toad quarantine efforts across Australian offshore islands and in a proposed mainland containment strategy.

Managing biological invasions in urban environments with the acceptance sampling approach

Detections of invasive species outbreaks are often followed by the removal of susceptible host organisms in order to slow the spread of the invading pest population. We propose the acceptance sampling approach for detection and optional removal of susceptible host trees to manage an outbreak of the emerald ash borer (EAB), a highly destructive forest pest, in Winnipeg, Canada. We compare the strategy with two common delimiting survey techniques that do not consider follow-up management actions such as host removal. Our results show that the management objective influences the survey strategy. The survey-only strategies maximized the capacity to detect new infestations and prioritized sites with high likelihood of being invaded. Comparatively, the surveys with subsequent host removal actions allocated most of the budget to sites where complete host removal would minimize the pest's ability to spread to uninvaded locations. Uncertainty about the pest's spread causes the host removal measures to cover a larger area in a uniform spatial pattern and extend to farther distances from already infested sites. If a decision maker is ambiguity-averse and strives to avoid the worst-case damages from the invasion, the optimal strategy is to survey more sites with high host densities and remove trees from sites at farther distances, where EAB arrivals may be uncertain, but could cause significant damage if not detected quickly. Accounting for the uncertainty about spread helps develop a more robust pest management strategy. The approach is generalizable and can support management programs for new pest incursions.