Invasive species can cause substantial reductions in a region’s ecological, industrial, and human welfare, and often require significant control expenditures. The Emerald Ash Borer, for example, established itself in the United States in the early 1990s and has caused the death of hundreds of millions of ash trees and is estimated to cause billions of dollars of costs and damages annually. Currently, New Zealand is threatened by the establishment of similar wood borers and bark beetles, pests that pose a substantial threat to the country’s forest industry, as well as to its urban and natural forests. In a new journal article, with New Zealand colleagues Eckehard G. Brockerhoff (Scion), John M. Kean (AgResearch), and James A. Turner (AgResearch), we ask: How much should New Zealand invest in surveillance, and where should traps be placed, to cost-efficiently monitor for new wood borer and bark beetle invasions?
To control newly established invasions and limit their damages, new populations must first be detected. And the sooner that an invasion is detected, the less expensive and more successful eradication or control efforts are likely to be—lessening the costs and damages associated with the invading species. Greater investment in surveillance upfront reduces the expected costs and damages from new invasions, but this can be quite costly; and gauging what level of surveillance spending will produce the most cost-effective results, and where on the landscape those resources should be targeted, is difficult for managers and policymakers to assess.
To determine how surveillance efforts should be best allocated across a landscape and how much total resources should be invested, we developed a bioeconomic model with the goal of designing a program that minimizes the total combined costs of surveillance investments, invasive species control efforts, and damages from invasions that establish. The model accounts for diversity across a landscape, in terms of the likelihood of invasive species introduction, the costs of surveillance, and the amount of resources at risk from invasive species establishment. It also considers a suite of potential invasive species that could arrive and that differ in their anticipated rate of spread and damages to at-risk resources. Our approach assumes that greater intensities of surveillance increase the likelihood of earlier detection and that the size of a population when detected affects both the costs of eradication and likelihood of success.
For New Zealand, we used the model to design a long-term, trap-based surveillance program for early detection of wood borers and bark beetles. Currently, such a program does not exist for these species. Our results indicate that an optimal surveillance strategy for detection would require a very high investment in traps—over 10,000 deployed annually for 30 years—at a cost of about US$54 million, eventually producing expected net benefits of about US$300 million. However, we also found that implementing a far more limited surveillance program (a not-unlikely scenario, due to budget constraints that would limit the number of traps deployed) would still provide positive net benefits. We identified how traps should be allocated across New Zealand for a range of surveillance budgets and found that surveillance should be targeted most intensively around import-heavy areas, which are expected to have high rates of invasion introduction, and in places that have the potential to accrue damages quickly (for example, due to their proximity to at-risk resources). We expect this pattern to also apply outside of New Zealand as well.
Although the cost-efficiency of surveillance will vary depending on the magnitude of the program, our findings show that New Zealand would indeed benefit from investment in a trap-based surveillance program, even if investments were relatively small. The intensity of such a program could be scaled up as more funds become available, and our approach can be used to guide how traps should be allocated across the region to provide the greatest benefits.
