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Keywords:

  • Epistemic uncertainty;
  • human-assisted spread;
  • invasibility;
  • mean-variance frontier;
  • road network;
  • stochastic dominance

Abstract

Aim

Geographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines risk and uncertainty in a single map product.

Location

Canada.

Methods

We divide the study area into a set of spatial domains and treat each domain as an individual ‘portfolio’ with a unique distribution of the expected impacts of invasion. The risk of invasion is then mapped by finding nested ‘efficient’ portfolio sets that identify the geographic areas exhibiting the worst combinations of the estimated risk of invasion and the uncertainty in that estimate. For Canadian municipalities, we apply the approach to quantify the risk that a given location will receive invasive forest pests with commercial freight transported via the North American road network. We compare risk allocation techniques that employ the concepts of nested mean-variance (M-V) frontiers and second-degree stochastic dominance.

Results

While both methods based on M-V and the stochastic dominance principles identified similar areas of highest risk, they differed in how they demarcated moderate-risk areas. Furthermore, they address uncertainty in different ways, treating it as a risk premium (in the case of nested M-V frontiers) or producing risk-averse delineations (in the case of stochastic dominance).

Main conclusions

The portfolio-based approach offers a viable strategy for dealing with the typically wide variability in risk estimates caused by a lack of knowledge about a new invader. The methodology also provides a tractable way of incorporating decision-making preferences into the final risk estimates and thus better aligns risk assessments with particular decision-making scenarios about the organism of concern.