Disentangling complex biological introduction histories: the ongoing invasion of the brown anole (Anolis sagrei)

Abstract

Species introductions leave genetic signatures that reflect the pattern of introduction history. These are shaped by the rate and timing of propagules, levels of admixture, genetic drift, population bottlenecks, and selection in the new environment. Deciphering these complex genetic signatures for recent human-mediated introductions is often challenging because realistically complex inference models are often unavailable. Here, we employed approximate Bayesian computation (ABC) approaches, which rely on simulation and rejection sampling to bypass the need for arbitrarily complex likelihood functions. With a focus on the invasion of the brown anole ( Anolis sagrei ) in the US, we draw on historical data, prior genetic studies, and population genetic and phylogenetic tools to develop a set of demographic models of invasion in major areas of the species’ introduced range and select among them using ABC. Our analyses reveal a complex invasion history involving repeated introductions and admixture in introduced areas. Strong evidence indicates the brown anole is spreading throughout the US mainland and Hawaiʻi in part through the nursery trade and is accumulating high genetic diversity from multiple sources. Given its success in the Southeastern US and Hawaiʻi, the potential for a widespread establishment in Southern California, where the invasion is at its early stages, raises significant concern. We underscore the need for management strategies to mitigate the potential ecological impacts of the brown anole invasion in new territories in California and across the Pacific. As the number and scale of biological invasions continue to grow, there is an increasing need for robust and scalable methods that can provide information about how they proceed. With the brown anole as a model for complex biological invasions, this study provides a framework for inferring the genetic and demographic intricacies of increasingly complex species invasions.

The authors' abstract, as published at the source. Biological Invasions, 2026 · DOI ↗