Using ABC and microsatellite data to detect multiple introductions of invasive species from a single source

Area of Science:

• Molecular Ecology
• Population Genetics
• Conservation Biology

Background:

• Biological invasions pose significant threats to biodiversity, agriculture, and public health.
• Determining introduction routes and modalities is crucial for managing invasive species.
• Approximate Bayesian computation (ABC) is a key statistical framework for inferring invasion history.

Purpose of the Study:

• To present and evaluate a novel model for biological invasions involving multiple introductions from a single source (MISS).
• To assess the effectiveness of ABC in distinguishing between single and multiple introduction events using simulated microsatellite data.
• To apply the MISS model and ABC to real-world invasion data, specifically focusing on bumblebee introductions in New Zealand.

Main Methods:

• Simulated microsatellite data under various demographic parameters to test the MISS model.
• Application of ABC to microsatellite data from three invasive bumblebee species in New Zealand.
• Evaluation of different summary statistics selection methods for ABC analysis.

Main Results:

• Simulations showed good ability to differentiate between one- and two-wave introduction models across a broad parameter range, largely independent of summary statistics.
• Parameter estimation using ABC was generally accurate, with exceptions noted for bottleneck timing.
• The MISS model was rejected for one bumblebee species, while results for the other two were inconclusive.

Conclusions:

• The MISS model offers a valuable framework for studying complex invasion histories, potentially improving management strategies.
• Detecting multiple introduction waves is critical as they can influence genetic diversity, inbreeding, and invasion impact.
• Further theoretical and empirical research incorporating the MISS model is recommended for advancing invasion science.