Predicting the dispersal of wild Pacific oysters Crassostrea gigas (Thunberg, 1793) from an existing frontier population—a numerical study

• Published in: Aquatic Invasions
• Main Authors: Robins, Peter, King, Jonathan, Jenkins, Stuart, Tita, Andrei
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: Biophysical Model; Non-native aquaculture species; Larval transport; Population Spread; Irish Sea; Pacific; Crassostrea gigas; Pacific oyster
• Online Access: https://research.bangor.ac.uk/portal/en/researchoutputs/predicting-the-dispersal-of-wild-pacific-oysters-crassostrea-gigas-thunberg-1793-from-an-existing-frontier-populationa-numerical-study(0b0bc7c7-3f4c-4c1f-866c-d087e59d78e8).html; https://doi.org/10.3391/ai.2017.12.2.01; https://research.bangor.ac.uk/ws/files/17730436/Robins_etal_2017_AquaticInvasions.pdf

Non-native populations of Pacific oysters Crassostrea gigas (Thunberg 1793) are established around the United Kingdom (UK), with two genetically different stocks originating from separate introductions to the UK and France. In this study, we use a coupled biophysical model to simulate Pacific oyster larval transport, in order to investigate the dispersal of the species from a known population near their northern limit on the west coast of the UK (in the Milford Haven Estuary). The model included a pelagic phase, simulating different swimming behaviours, and a settlement phase based on a hydrospatial substrate map. Following successful settlement elsewhere, subsequent releases simulated potential population spread over successive generations. Our results suggest that, should there be sufficiently warm sea temperatures to allow reproduction, dispersal away from Milford Haven Estuary would most be southeast ward towards the Bristol Channel; but dispersal north and west to Ireland is also possible, depending heavily on pelagic swimming behaviour. Seasonal modifications to circulation were less influential. Our study increases understanding of factors that contribute to oyster population spread, and suggests methods for improved management through numerical predictions.