Dispersal capacity of Monochamus galloprovincialis, the European vector of the pine wood nematode, on flight mills

Abstract

The pine wood nematode (Bursaphelenchus xylophilus), which causes the symptoms of pine wilt disease, is recognized worldwide as a major forest pest. It was introduced into Portugal in 1999. It is transmitted between trees almost exclusively by longhorn beetles of the genus Monochamus, including, in particular, M. galloprovincialis (Coleoptera: Cerambycidae) in maritime pine forests. Accurate estimates of the flight capacity of this insect vector are required if we are to understand and predict the spread of pine wilt disease in Europe. Using computer‐linked flight mills, we evaluated the distance flown, the flight probability and speed of M. galloprovincialis throughout adulthood and investigated the effects of age, sex and body weight on these flight performances, which are proxies for dispersal capacity. The within‐population variability of flight performance in M. galloprovincialis was high, with a mean distance of 16 km flown over the lifetime of the beetle. Age and body weight had a significant positive effect on flight capacity, but there was no difference in performance between males and females. These findings have important implications for managing the spread of the pine wood nematode in European forests.

Dispersal differences of a pest and a protected Cerambyx species (Coleoptera: Cerambycidae) in oak open woodlands: a mark–recapture comparative study

“…Poisson and negative binomial models indicated that dispersal distances were an aggregative distribution, which is attributed in part to long-distance dispersal movements being more frequent than expected. Note also that the frequency of long flyers is likely to be higher than recorded because mark-recapture methods may underestimate long-distance dispersal as longer movements are scarce and characterised by a high degree of stochasticity (Smith et al, 2001;Drag et al, 2011;Chiari et al, 2013;David et al, 2013;Elek et al, 2014;Etxebeste et al, 2016). An aggregated distribution of dispersal distances supports previous suggestions that two adult morphs differing in dispersal behaviour, short (resident) and long (transient) flyers, may occur within cerambycid populations (Bancroft & Smith, 2005;López-Pantoja et al, 2008;Torres-Vila et al, 2012).…”

Dispersal Patterns of Pine Wilt Disease in the Early Stage of Its Invasion in South Korea

“…The relative frequency of SDD is higher than those of LDD and human-mediated dispersal distance because 89% of dispersal distances were less than 1 km/year. David et al [14] reported that the mean dispersal distance of M. galloprovincialis, a main European vector of PWD, was 16 km over the lifetime of the beetle on the basis of flight mill experiments. A modeling study showed that the maximum dispersal distance was 464 m, whereas a lifetime dispersal distance of M. galloprovincialis was observed in the range of 107 to 122 m in the field [25].…”

“…The foraging behaviors of Monochamus species, including M. alternatus, have been intensively studied because these species are the major vectors of PWD in many countries: M. alternatus in Japan [21][22][23], M. galloprovincialis in Europe [14,24,25], and M. carolinensis in America [26]. Flight distances Figure 5.…”

“…The dispersal distance of PWD depends on the dispersal capacity of Monochamus beetles. Several approaches have been used to measure the dispersal capacity of PWD, including analysis of field occurrence data [2], and flight-mill experiments [14]. Therefore, it is important to identify dispersal patterns of PWD as well as major environmental factors influencing the dispersal speed for the management of PWD [15].…”

Dispersal of individuals of the flightless grassland ground beetle, Carabus hungaricus (Coleoptera: Carabidae), in three populations and what they tell us about mobility estimates based on mark-recapture

“…Compared to other methods, such as population genetic analyses, telemetry, and dispersal simulators (i.e. flight-mills), the MRRbased results tend to give lowest dispersal estimates due to underestimation of long-distance movements (Koenig et al, 1996;Jonsell et al, 2003;Drag et al, 2011;Chiari et al, 2013;David et al, 2013;Oleksa et al, 2013), and vary due to local conditions and/or sampling designs (e.g. Stevens et al, 2010;Hassal & Thompson, 2012).…”