Influence of Trap Distance From a Source Population and Multiple Traps on Captures and Attack Densities of theRedbay Ambrosia Beetle(Coleoptera: Curculionidae: Scolytinae)

Abstract

The redbay ambrosia beetle,Xyleborus glabratusEichhoff, is the principal vector of laurel wilt disease in North America. Lures incorporating essential oils of manuka plants (Leptospermum scopariumJ. R. Forster& G. Forster) or cubeb seeds (Piper cubebaL.f.) are the most effective in-flight attractants to date. Using grids of traps baited with these essential oil lures, we evaluated 1) the effect of trap distance from a source beetle population on beetle captures, 2) the feasibility of trapping out low-density beetle populations, and 3) the effect of trap spacing on beetle captures. In the first experiment, increasing trap distance up to 300 m from a sourceX. glabratuspopulation had little effect on beetle captures. In a second experiment conducted in a study area with very low beetle densities, trapping for 5 mo prior to deploying freshly cut, uninfested redbay bolts had no effect on subsequent attack densities. In a third experiment, numbers ofX. glabratuscaptured in traps in the center of a grid of nine traps spaced 1 or 5 m apart were compared with lone baited or unbaited traps 30 m away. Relative to the more distant traps, the grid of baited traps neither increased captures in the unbaited center trap nor decreased captures in the baited center trap, regardless of spacing. The results suggest that the effective trapping distance of essential oil lures forX. glabratusis <1 m, and that newer, more attractive lures will be needed to be useful in managingX. glabratuspopulations.

Host density dependence and environmental factors affecting laurel wilt disease incidence

“…Our study found a consistent dispersal kernel captured at a relatively large scale (c.5 km). Hanula et al (2016) monitored X . glabratus populations as they dispersed from a point source in a natural forest setting and found that dispersal was flat across a relatively long range (<300 m).…”

“…The densities of susceptible host plants in those patches and the distances between patches probably affect the rate of disease spread across a landscape. Redbay ambrosia beetles can disperse over hundreds of metres (Hanula et al, 2016), and related scolytid bark beetles are known to disperse up to tens of kilometres, depending on wind speeds and availability of suitable host materials. Using analogous and better‐studied systems like bark beetles to infer potential epidemiological parameters for ambrosia beetles can help develop hypotheses for how the laurel wilt disease pathosystem may operate; however, bark beetles sometimes differ from ambrosia beetles in key ways, including attraction to hosts, food source, and presence of fungal symbionts (Hulcr & Stelinski, 2017).…”

Host density dependence and environmental factors affecting laurel wilt invasion

“…Our study found a consistent dispersal kernel captured at a relatively large scale (~5km). Hanula et al (2016) monitored X. glabratus populations as they dispersed from a point source in a natural forest setting and found that dispersal was flat across a relatively long range (<300m). Recent work by Seo et al (2017) suggested that the vast majority of flights completed by X. glabratus are short range (<10m), and only a few individuals fly over a longer range.…”

“…The densities of susceptible host plants in those patches and the distances among patches likely affect the rate of disease spread. Redbay ambrosia beetles can disperse over hundreds of meters (Hanula et al 2016), and related scolytids are known to disperse up to tens of kilometres, depending on wind speeds and availability of suitable host materials (Barak et al 2018;Botterweg 1982). Many environmental factors like windspeed and precipitation can favour or limit the dispersal of plant pathogens and their vectors (Narouei-Khandan et al 2017;Shimwela et al 2019;Shimwela et al 2018), and prolonged environmental stress such as drought can predispose trees to bark beetle attacks and subsequent disease development (Anderegg et al 2013;Kelsey et al 2014;Wong and Daniels 2017).…”

Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

“…It is mainly based on the twodimensional relational table, which is composed of land price data and various factor data affecting land price data, including land price and sampling point number. , address, coordinates, distance from the point to the city center, distance from the point to the park, population density, distance from the point to the school, distance from the point to the mountain, distance from the bus stop, distance from the point to the hospital [Hanula, Mayfield and Reid (2016)], point to the main road The distance, the distance from the point to the highway, the distance from the point to the river, the number of floors, the base area, and the floor area of the building, and 29 attribute fields, and 713 records. Because it is the data set collected and calculated in the field, rather than the standard test data set, the data itself is different from the general standardized data, there are certain errors, and there are certain limitations in the data quality.…”