Statistical Phylogeography

By L. Knowles, Wayne P. Maddison. Molecular Ecology, 2002

788 citations recorded by the source corpus.

While studies of phylogeography and speciation in the past have largely focused on the documentation or detection of significant patterns of population genetic structure, the emerging field of statistical phylogeography aims to infer the history and processes underlying that structure, and to provide objective, rather than ad hoc explanations. Methods for parameter estimation are now commonly used to make inferences about demographic past. Although these approaches are well developed statistically, they typically pay little attention to geographical history. In contrast, methods that seek to reconstruct phylogeographic history are able to consider many alternative geographical scenarios, but are primarily nonstatistical, making inferences about particular biological processes without explicit reference to stochastically derived expectations. We advocate the merging of these two traditions so that statistical phylogeographic methods can provide an accurate representation of the past, consider a diverse array of processes, and yet yield a statistical estimate of that history. We discuss various conceptual issues associated with statistical phylogeographic inferences, considering especially the stochasticity of population genetic processes and assessing the confidence of phylogeographic conclusions. To this end, we present some empirical examples that utilize a statistical phylogeographic approach, and then by contrasting results from a coalescent-based approach to those from Templeton's nested cladistic analysis (NCA), we illustrate the importance of assessing error. Because NCA does not assess error in its inferences about historical processes or contemporary gene flow, we performed a small-scale study using simulated data to examine how our conclusions might be affected by such unconsidered errors. NCA did not identify the processes used to simulate the data, confusing among deterministic processes and the stochastic sorting of gene lineages. There is as yet insufficient justification of NCA's ability to accurately infer or distinguish among alternative processes. We close with a discussion of some unresolved problems of current statistical phylogeographic methods to propose areas in need of future development.

Subfields: Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2ee6ebcbb3664c6cf27ee3a5e7382caef3ad16e6

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In Nature and Landscape Conservation

Citations in the corpus

Eastward Ho: phylogeographical perspectives on colonization of hosts and parasites across the Beringian nexus (2007, background): “Nested clade analysis (Templeton, 1998) uses parsimony networks and geographical distances to infer biogeographical patterns, although there are pitfalls in the interpretation of nested clade analysis (Knowles & Maddison, 2002; but see Templeton, 2004).”
MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity (2011, background): “…a standard tool for understanding the interplay between geography, climate change, speciation, extinction, demographic changes, and species interactions as well as making links between different types of biodiversity, ecological services and ultimately well-informed conservation policy [62,63].”
Phylogeography: retrospect and prospect (2009, background): “The net goal should be a robust ‘statistical phylogeography’ (Knowles & Maddison, 2002; Templeton, 2004; Knowles, 2008) that reflects a realistic integration of population genetic and phylogenetic methods.”
Statistical phylogeography: methods of evaluating and minimizing inference errors (2004, methodology): “The realm of biological possibilities being considered is explicit in the a posteriori inference key, and it is implicit in the statistical phylogeography sensu Knowles & Maddison (2002) by examining the alternatives that were simulated.”
Molecular phylogenetics: principles and practice (2012, background): “The third area of exciting development, which was mentioned at the beginning of this Review, is statistical phylogeography 20,114–116 .”
Phylogeographic studies of plants in China: Advances in the past and directions in the future (2012, background): “However, this approach may support false hypotheses and it also lacks strict tests in some scenarios (Knowles & Maddison, 2002; Templeton, 2004, 2008, 2010; Nielsen & Beaumont, 2009; Beaumont et al., 2010).”
Inferring phylogeny despite incomplete lineage sorting. (2006, background): “Such discordances are expected in typical studies, both between any single gene tree and the species history, and between different loci (Takahata, 1989; Rosenberg, 2003; Knowles and Maddison, 2002).”
Phylogenetic Methods in Biogeography (2011, background): “This is the domain of phylogeography and population genetics, the fields studying the processes that influence the spatial distribution and genetic variation within and among closely related taxa (Knowles 2009).”
Late-Quaternary biogeographic scenarios for the brown bear (Ursus arctos), a wild mammal model species (2011, background): “It is widely acknowledged that phylogeographic interpretation can benefit from the incorporation of information from climatology, paleontology and ancient DNA analysis in addition to the traditional approaches of population genetics and phylogenetics (e.g., Knowles, 2009).”
Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses (2007, methodology): “However, an excessively complex model may have limited utility because the available genetic data for evaluating such models may not be sufficient – complex models can require large amounts of genomic data (Knowles & Maddison, 2002).”
Reconstructing routes of invasion using genetic data: why, how and so what? (2010, methodology): “This stochasticity hence limits the validity of the results obtained by the indirect genetic methods described earlier, for which there is no simple method for estimating the probability of error (e.g. Knowles & Maddison 2002).”
High genetic diversities and complex genetic structure in an Indo-Pacific tropical reef fish (Chlorurus sordidus): evidence of an unstable evolutionary past? (2004, background): “NCA does not, however, calculate statistical uncertainty associated with models and does not test to differentiate among alternative outcomes (Knowles and Maddison 2002).”
Historical biogeography and post‐glacial recolonization of South American temperate rain forest by the relictual marsupial Dromiciops gliroides (2008, background): “The distribution of populations across the geographical range of a contemporary species is the result of historical processes, the influence of which can be inferred from patterns of genetic population structure and phylogenetic lineages (Avise, 2000; Knowles & Maddison, 2002).”