Encyclopedia of Quantitative Risk Analysis and Assessment

Sereika, Susan M.; Walls, Lesley; Piegorsch, Walter W.; Chan, Ngai Hang; Melnick, Edward L.; Denuit, Michel; Coolen, Frank P. A.; Cox, Tony; Paustenbach, Dennis J.; Roberts, Jennifer D.; Everitt, Brian S.; French, Simon

doi:10.1002/9780470061596

Cited by 35 publications

(1 citation statement)

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“…Commonly, the estimation of the "tail" of the dispersal distribution is a problem in ecology, and the normal distribution may underestimate dispersal if long-distance dispersal occurs frequently Turchin, 1997). The Laplace distribution has so-called heavy tails, unlike the normal distribution (Kotz, Kozubowski, & Podgorski, 2001;Melnick & Everitt, 2008; Figure 1). This is appropriate for modelling ALB dispersal as some individuals can spread over long distances, in extreme cases more than one or two kilometres, or even more according to flight mill experiments (Smith et al, , 2004 computed a numerical simulation of ALB spread that reinforces our idea that the Laplace distribution was appropriate (see Appendix S1).…”

Section: Mathematical Approach Presentationmentioning

confidence: 99%

A local risk map using field observations of the Asian longhorned beetle to optimize monitoring activities

Fragnière¹,

Forster

Hölling

et al. 2018

J Applied Entomology

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The Asian longhorned beetle Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) is one of the most dangerous xylophagous pests affecting broadleaf trees in the world. Eradication programmes are undertaken in non‐native regions, requiring extensive resources and involving high costs. An adapted strategy must be set up to optimize the ratio cost/probability of success. We developed a method to generate a risk index of A. glabripennis presence at a local scale, in the surrounding area of an infestation, using field observations (counts of adult insects, exit holes and infested trees). The method, mathematically based on the bivariate symmetric Laplace distribution, has thus reasonable input requirements. The output risk map is easy to interpret and can be directly used by decision‐makers. We used our approach in three infestations in Switzerland. The risk map represented well the insect pressure (beetle population density). We highlighted the fact that survey boundaries, commonly chosen using constant distances from the infestation, should be selected regarding the spatial distribution of the insect pressure, to prioritize monitoring activities. The risk map provides a helpful instrument for advanced survey planning after a first overview, for example to decide which area and which host trees should be inspected for infestations.

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Section: Mathematical Approach Presentationmentioning

confidence: 99%