Clustering and Assignment Methods in Landscape Genetics

Abstract

Clustering and assignment methods that provide synthetic representations of population genetic structure and evaluate membership of individuals to a fixed number of genetic clusters are essential tools of landscape genetic researchers. This chapter reviews statistical methods that ascertain population structure and estimate genetic ancestry without the use of predefined populations. It summarizes 10 methods that synthesize concepts from population genetics, landscape ecology, and landscape genetics, and that integrate geographic and ecological information on population samples. The chapter describes two general approaches called exploratory data analysis (EDA) and model-based clustering (MBC). A classical MBC method is implemented in the computer program STRUCTURE. The chapter focuses on incorporating spatial and environmental heterogeneity into population genetic analysis, and surveys the abilities of EDA and MBC methods to achieve this objective. It finally presents remaining challenges and future opportunities for clustering and assignment methods.