DOI: 10.5281/zenodo.17180520
title: Input and output files for DIYABC-RF analyses in the manuscript: "Population genomics traces zebra mussel invasions along the expanding western North American front in Minnesota, USA to their source waters."

description: Manuscript title:

"Population genomics traces zebra mussel invasions along the expanding western North American front in Minnesota, USA to their source waters"

Abstract:

Zebra mussels (ZMs) continue to transform aquatic ecosystems, threaten native species, and accrue high economic costs in the Northern Hemisphere. In 2024, Minnesota (MN) in the western Great Lakes became the state with the most ZM-infested lakes in the USA. We used population genomics to examine recent ancestry and infer the source water bodies from which ZMs colonized >1/3 of MN lakes infested from 2003-2018 (when spread accelerated), then compared results to traffic between lakes of boaters and anglers, the suspect invasion vectors. Lake Superior, the Upper Mississippi River, Lake Erie, and several inland lakes were our top-ranked inferred sources. We traced ZMs in 51 of 58 infested lakes to source water bodies in-state, but most were not from alleged “superspreaders” (sensu epidemiology). Mille Lacs Lake, a popular angling destination and boater network hub, was a notable exception as the inferred source for three lakes. In three MN lake-rich regions in which spread continues to be concentrated, invasions from nearby were common, and sources were most often not high boat-traffic lakes, suggesting that vectors other than trailered boats need further evaluation. Geographic expansion of our population genomic dataset could provide genomic surveillance and guide prevention of the continuing spread of ZMs.

 
description: Overview

We used DIYABC-RF to evaluate competing scenarios for invasion of Minnesota waterbodies. This program employs simulation-based Approximate Bayesian Computation (ABC) to construct scenarios of biological invasions, with their complex histories of colonization, population bottlenecks, divergence, and admixture events, combined with supervised machine learning (SML) methods to select among competing scenarios or to estimate parameters. Random Forest is an efficient SML approach in cases such as this one, in which large panels of individuals from numerous populations, genotyped at thousands of SNP loci are the data upon which scenario choice is based.

 

DIYABC-RF invasion model formulation and scenario testing

Our invasion scenarios tested in DIYABC-RF are alternative descriptions of the history of colonization of water bodies by zebra mussels. This history takes the form of evolutionary models that consider four categories of events, interspersed by time periods of independent evolution. First is population divergence. This occurs after infestation of a new waterbody and is represented as a splitting event on a population genealogy in which the source population separates into two descendant populations, one that remains in the source waterbody and the other that has colonized the destination waterbody. No gene flow is permitted between source and destination waterbody after colonization. The three other categories of events depicted in the scenarios are changes in population size, admixture events, and sampling from populations.

The ABC part of DIYABC-RF performs coalescent simulations on the data (genotypes at all SNP loci from each mussel) to generate datasets known as training sets—these are the data sets that the Random Forest module uses to “learn” relationships between the simulated molecular data and the simulated genealogies. Coalescent models are formulated by ordering events back in time, from present day back to the time when all lineages have coalesced into a single common ancestral lineage. A splitting event, running backwards in time, is represented as a “merge” event—in which the gene lineages in the colonized lake and in the source-lake coalesce to form ancestral lineages.
description: Each of the zip archives contains data files, input and output files from each of six lake regions described in the manuscript. Each subfolder (within each lake region) contains the files corresponding to a single waterbody.

The four input files are named <filename>.snp (the SNP data file), headerRF.txt, reftableRF.bin, and statobsRF.txt.  These are all that are needed to launch an analysis and recreate our results.

Six of the output files (with file names beginning with "modelchoice_analysis") are provided from each analysis. In addition, there is a PDF file that provides an evaluation of the compatibility of the priors and formulated scenarios with the observed dataset.  The figure is a projection of the data from the simulations (i.e. the training set) and the observed data on the first two Linear Discriminant Analysis (LDA) axes (Collin et al. 2021a, 2021b). Combinations of scenarios and priors that do not yield an LDA plot in which the observed data falls within the vicinity of these clouds of points are unreliable, and no inferences should be drawn in such cases. Several exploratory scenario testing attempts were discarded because they failed this LDA-plot criterion.