Population genetic structure of the predatory, social wasp Vespula pensylvanica in its native and invasive range

Abstract

Invasive species cause extensive damage to their introduced ranges. Ocean archipelagos are particularly vulnerable to invasive taxa. In this study, we used polymorphic microsatellite markers to investigate the genetic structure of the social wasp Vespula pensylvanica in its native range of North America and its introduced range in the archipelago of Hawaii. Our goal was to gain a better understanding of the invasion dynamics of social species and the processes affecting biological invasions. We found that V. pensylvanica showed no significant genetic isolation by distance and little genetic structure over a span of 2000 km in its native range. This result suggests that V. pensylvanica can successfully disperse across large distances either through natural‐ or human‐mediated mechanisms. In contrast to the genetic patterns observed in the native range, we found substantial genetic structure in the invasive V. pensylvanica range in Hawaii. The strong patterns of genetic differentiation within and between the Hawaiian Islands may reflect the effects of geographic barriers and invasion history on gene flow. We also found some evidence for gene flow between the different islands of Hawaii which was likely mediated through human activity. Overall, this study provides insight on how geographic barriers, invasion history, and human activity can shape population genetic structure of invasive species.

Lack of genetic structuring, low effective population sizes and major bottlenecks characterise common and German wasps in New Zealand

“…In both species, the genetic variation within New Zealand was not sufficient to distinguish between meta-populations. Low levels of genetic structure found in our study contrast with studies on invasive Vespula wasp populations in Australia (Goodisman et al 2001) and Hawaii (Chau et al 2015) but are in line with work on invasive paper wasp populations, Polistes spp., in New Zealand (Tsuchida et al 2014). The difference between our results and the findings from the Hawaiian Islands and Australia might be due to weaker dispersal barriers among sample sites in New Zealand.…”

“…Our findings indicate a strong genetic bottleneck for V. germanica and V. vulgaris in their invaded range of New Zealand and suggest that New Zealand populations were founded by a small number of individuals. The reduction of genetic diversity through bottleneck effects in invasive Vespula wasps is in line with studies on other invasive social wasps (Goodisman et al 2001;Husseneder et al 2012;Tsuchida et al 2014;Arca et al 2015;Chau et al 2015;Cheng et al 2016;Takeuchi et al 2017).…”

Ghosts from the past: even comprehensive sampling of the native range may not be enough to unravel the introduction history of invasive species—the case of Acacia dealbata invasions in South Africa

“…To better understand the introduction history of A. dealbata in South Africa, Approximate Bayesian Computation (ABC) analyses (Beaumont et al., ; Beaumont, ) were done using the software DIYABC (version 2.1.0; Cornuet et al., ). This approach allows the simulation of a large number of genetic datasets for different potential introduction scenarios that are compared with the observed dataset to identify the most likely scenario (Beaumont, ; Chau et al., ; Barker et al., ). To define potential native source regions in Australia, native populations were pooled into population genetic clusters identified by Hirsch et al.…”

Genetic analyses reveal complex introduction histories for the invasive tree Acacia dealbata Link around the world

“…To test different introduction scenarios to each of the different nonnative ranges of A. dealbata included here, we applied Approximate Bayesian Computation (ABC) analyses (Beaumont, 2010;Beaumont et al, 2002) using the software DIYABC (version 2.1.0; Cornuet et al, 2015). With this approach, a large number of genetic datasets can be simulated for a set of potential introduction scenarios which are then compared to the observed data to determine the most likely scenario (Barker et al, 2017;Beaumont, 2010;Chau et al, 2015). We specified different introduction scenarios for each non-native range using the approach of Hirsch et al (2019).…”