A new multicriteria risk mapping approach based on a multiattribute frontier concept.

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components' importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high-risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info-gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.

Exploring critical uncertainties in pathway assessments of human-assisted introductions of alien forest species in Canada.

Long-distance introductions of alien species are often driven by socioeconomic factors, such that conventional "biological" invasion models may not be capable of estimating spread fully and reliably. In this study, we demonstrate a new technique for assessing and reconstructing human-mediated pathways of alien forest species entries to major settlements in Canada via commercial road transportation and domestic trade. We undertook our analysis in three steps. First, we used existing data on movement of commodities associated with bark- and wood-boring forest pests to build a probabilistic model of how the organisms may be moved from one location to another through a transportation network. We then used this model to generate multiple sets of predictions of species arrival rates at every location in the transportation network, and to identify the locations with the highest likelihood of new incursions. Finally, we evaluated the sensitivity of the species arrival rates to uncertainty in key model assumptions by testing the impact of additive and multiplicative errors (by respectively adding a uniform random variate or symmetric variation bounds to the arrival rate values) on the probabilities of pest transmission from one location to another, as well as the impact of the removal of one or more nodes and all connecting links to other nodes from the underlying transportation network. Overall, the identification of potential pest arrival hotspots is moderately robust to uncertainties in key modeling assumptions. Large urban areas and major border crossings that have the highest predicted species arrival rates have the lowest sensitivities to uncertainty in the pest transmission potential and to random changes in the structure of the transportation network. The roadside survey data appears to be sufficient to delineate major hubs and hotspots where pests are likely to arrive from other locations in the network via commercial truck transport. However, "pass-through" locations with few incoming and outgoing routes can be identified with lower precision. The arrival rates of alien forest pests appear to be highly sensitive to additive errors. Surprisingly, the impact of random changes in the structure of the transportation network was relatively low.