Biological Invasions

, Volume 1, Issue 1, pp 89–96

When to Ignore Advice: Invasion Predictions and Decision Theory

  • C.S. Smith
  • W.M. Lonsdale
  • J. Fortune

DOI: 10.1023/A:1010091918466

Cite this article as:
Smith, C., Lonsdale, W. & Fortune, J. Biological Invasions (1999) 1: 89. doi:10.1023/A:1010091918466


Organisms generally become pests at a low rate. As a consequence of this low ‘base-rate probability’, the large majority of organisms rejected in any random sample of potential introductions would probably be harmless, despite the fairly high accuracy of some recently proposed risk assessment systems for exotic introductions. Here we distinguish between a system's accuracy (the proportion of a group of known pest species that would be correctly identified as pests) and reliability (the rate of false positives and false negatives produced once the base-rate is taken into account). We next adapt a decision theory analysis of earthquake prediction to explore when we would be best advised to ignore the recommendations of a screening system for exotic introductions. In one scenario, we show that a pest risk assessment system with an accuracy of 85% would be better ignored, unless the damage caused by introducing a pest is eight times or more that caused by not introducing a harmless organism that is potentially useful. Furthermore, because of the base-rate effect, in certain situations it may be more efficient to focus on identifying potential invaders from amongst already naturalized species than from amongst species at the importation stage.

base-rate effect decision theory exotic organisms GMO loss structure quarantine risk assessment 

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • C.S. Smith
    • 1
  • W.M. Lonsdale
    • 1
  • J. Fortune
    • 2
  1. 1.CSIRO European LaboratoryCRC for Weed Management SystemsMontferrier sur LezFrance
  2. 2.CRC for Weed Management SystemsUniversity of AdelaideGlen OsmondAustralia
  3. 3.CSIRO EntomologyCanberraAustralia

Personalised recommendations