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Mol Biol Evol. 2017 Apr 1;34(4):980-996. doi: 10.1093/molbev/msx050.

Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest.

Author information

1
Institut de Systématique, Évolution, Biodiversité, ISYEB - UMR 7205 - CNRS, MNHN, UPMC, EPHE, Muséum national d'Histoire naturelle, Sorbonne Universités, Paris, France.
2
INRA, Centre de Biologie et de Gestion des Populations (UMR INRA IRD Cirad Montpellier SupAgro), Montferrier-Sur-Lez, France.
3
Colorado State University, Fort Collins, CO.
4
Centre de Mathématiques et Informatique, Aix-Marseille Université, Marseille, France.
5
Institut Montpelliérain Alexander Grothendieck, Université de Montpellier, Montpellier, France.
6
Tropical Conservation Biology & Environmental Science, University of Hawaii at Hilo, HI.
7
Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude Bernard Lyon 1, Villeurbanne, France.
8
College of Plant Protection, Yunnan Agricultural University, Kunming, Yunnan Province, People's Republic of China.
9
Programa de Pós Graduação em Genética e Biologia Molecular, Programa de Pós Graduação em Biologia Animal, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
10
CIRAD, UMR Peuplement Végétaux et Bioagresseurs en Milieu Tropical, Paris, France.
11
Department of Entomology, University of Wisconsin, Madison, WI.
12
CABI, Delémont, Switzerland.
13
Graduate School of Environmental Earth Science, Hokkaido Daigaku University, Sapporo, Hokkaido Prefecture, Japan.
14
Department of Entomology, Cornell University, Ithaca, NY.
15
Dipartimento di Agronomia Animali Alimenti Risorse Naturali e Ambiente, Universita degli Studi di Padova, Padova, Italy.
16
Departament de Genètica, Universitat de Barcelona, Barcelona, Spain.
17
Division of Biological Sciences, University of California San Diego.
18
Mid-Columbia Agricultural Research and Extension Center, Oregon State University, Hood River, OR.
19
Department of Genetics, North Carolina State University, Raleigh, NC.
20
Department of Biological Sciences, Tokyo Metropolitan University, Tokyo, Japan.
21
MoA-CABI Joint Laboratory for Bio-safety, Chinese Academy of Agricultural Sciences, BeiXiaGuan, Haidian Qu, China.

Abstract

Deciphering invasion routes from molecular data is crucial to understanding biological invasions, including identifying bottlenecks in population size and admixture among distinct populations. Here, we unravel the invasion routes of the invasive pest Drosophila suzukii using a multi-locus microsatellite dataset (25 loci on 23 worldwide sampling locations). To do this, we use approximate Bayesian computation (ABC), which has improved the reconstruction of invasion routes, but can be computationally expensive. We use our study to illustrate the use of a new, more efficient, ABC method, ABC random forest (ABC-RF) and compare it to a standard ABC method (ABC-LDA). We find that Japan emerges as the most probable source of the earliest recorded invasion into Hawaii. Southeast China and Hawaii together are the most probable sources of populations in western North America, which then in turn served as sources for those in eastern North America. European populations are genetically more homogeneous than North American populations, and their most probable source is northeast China, with evidence of limited gene flow from the eastern US as well. All introduced populations passed through bottlenecks, and analyses reveal five distinct admixture events. These findings can inform hypotheses concerning how this species evolved between different and independent source and invasive populations. Methodological comparisons indicate that ABC-RF and ABC-LDA show concordant results if ABC-LDA is based on a large number of simulated datasets but that ABC-RF out-performs ABC-LDA when using a comparable and more manageable number of simulated datasets, especially when analyzing complex introduction scenarios.

KEYWORDS:

Drosophila suzukii; approximate Bayesian computation; invasion routes; population genetics; random forest

PMID:
28122970
PMCID:
PMC5400373
DOI:
10.1093/molbev/msx050
[Indexed for MEDLINE]
Free PMC Article

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