2010 journal article

Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest

RISK ANALYSIS, 30(2), 261โ€“276.

co-author countries: Canada ๐Ÿ‡จ๐Ÿ‡ฆ Israel ๐Ÿ‡ฎ๐Ÿ‡ฑ United States of America ๐Ÿ‡บ๐Ÿ‡ธ
author keywords: Decision theory; info-gap; robustness to uncertainty; Sirex noctilio; survey network
MeSH headings : Animals; Communication; Data Collection; Humans; Hymenoptera; North America; Plague; Population Groups; Probability; Risk; Risk Assessment; Trees; Uncertainty
Source: Web Of Science
Added: August 6, 2018

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.