Accommodating Phylogenetic Uncertainty in Evolutionary Studies

Huelsenbeck, John P.; Rannala, Bruce; Masly, John P.

doi:10.1126/science.288.5475.2349

Cited by 306 publications

(245 citation statements)

References 14 publications

(8 reference statements)

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“…Networks have been used to describe relationships within Celtic [30] and Indo-European languages [31]. New Bayesian MCMC (Markov chain Monte Carlo) methods approach the problem of phylogenetic uncertainty differently, by constructing a sample of trees in which trees are represented in proportion to their likelihood [32]. The proportion of trees in the sample on which a node is found is equivalent to its posterior probability (Figure 1).…”

Section: Cultures As Speciesmentioning

confidence: 99%

A phylogenetic approach to cultural evolution

Mace¹,

Holden²

2005

Trends in Ecology & Evolution

222

177

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Section: Cultures As Speciesmentioning

confidence: 99%

A phylogenetic approach to cultural evolution

Mace¹,

Holden²

2005

Trends in Ecology & Evolution

222

177

View full text Add to dashboard Cite

“…When the periods between the branching events in the true species tree are long in terms of numbers of generations (relative to the effective population size; §3a)-usually true in comparative analyses across species-this assumption is reasonable [60]. Methods have been developed that allow uncertainty in the topology of the working phylogeny to be incorporated into comparative analyses, either by repeating the analysis on a set of trees generated by bootstrapping [61] or by using Bayesian methods [62]. Assuming that the working phylogeny is accurate, calculation of appropriate contrasts will then depend on the fit between real evolutionary processes and the assumptions of the evolutionary model [37,44,51,52,63,64].…”

Section: Coping With Phylogenetic Non-independence In Cross-species Cmentioning

confidence: 99%

Controlling for non-independence in comparative analysis of patterns across populations within species

Stone¹,

Nee²,

Felsenstein

2011

Phil. Trans. R. Soc. B

135

127

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How do we quantify patterns (such as responses to local selection) sampled across multiple populations within a single species? Key to this question is the extent to which populations within species represent statistically independent data points in our analysis. Comparative analyses across species and higher taxa have long recognized the need to control for the non-independence of species data that arises through patterns of shared common ancestry among them (phylogenetic non-independence), as have quantitative genetic studies of individuals linked by a pedigree. Analyses across populations lacking pedigree information fall in the middle, and not only have to deal with shared common ancestry, but also the impact of exchange of migrants between populations (gene flow). As a result, phenotypes measured in one population are influenced by processes acting on others, and may not be a good guide to either the strength or direction of local selection. Although many studies examine patterns across populations within species, few consider such non-independence. Here, we discuss the sources of non-independence in comparative analysis, and show why the phylogeny-based approaches widely used in cross-species analyses are unlikely to be useful in analyses across populations within species. We outline the approaches (intraspecific contrasts, generalized least squares, generalized linear mixed models and autoregression) that have been used in this context, and explain their specific assumptions. We highlight the power of 'mixed models' in many contexts where problems of non-independence arise, and show that these allow incorporation of both shared common ancestry and gene flow. We suggest what can be done when ideal solutions are inaccessible, highlight the need for incorporation of a wider range of population models in intraspecific comparative methods and call for simulation studies of the error rates associated with alternative approaches.

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“…Historical inference invariably introduces some uncertainty into considerations of character evolution, the magnitude of which depends on how much information we have about the past. The increasing prevalence of Bayesian statistics and stochastic estimation methods is enabling uncertainty to be incorporated into many aspects of phylogenetic inference (e.g., see Huelsenbeck et al 2000). Here, the same approach is applied to the study of key innovations.…”

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confidence: 99%

Detecting the Historical Signature of Key Innovations Using Stochastic Models of Character Evolution and Cladogenesis

Ree¹

2005

Evolution

132

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Abstract. Phylogenetic evidence for biological traits that increase the net diversification rate of lineages (key innovations) is most commonly drawn from comparisons of clade size. This can work well for ancient, unreversed traits and for correlating multiple trait origins with higher diversification rates, but it is less suitable for unique events, recently evolved innovations, and traits that exhibit homoplasy. Here I present a new method for detecting the phylogenetic signature of key innovations that tests whether the evolutionary history of the candidate trait is associated with shorter waiting times between cladogenesis events. The method employs stochastic models of character evolution and cladogenesis and integrates well into a Bayesian framework in which uncertainty in historical inferences (such as phylogenetic relationships) is allowed. Applied to a well-known example in plants, nectar spurs in columbines, the method gives much stronger support to the key innovation hypothesis than previous tests. The tempo of evolution is a subject of general interest to evolutionary biologists, from nucleotide mutation rates within populations to the proliferation of branches on the tree of life. At the macroevolutionary level, key innovation hypotheses (Sanderson and Donoghue 1994) have been put forth to explain unusual disparities in species number between clades. A key innovation is commonly regarded as a biological trait that promotes lineage diversification via mechanisms that increase the rate of speciation and/or decrease the rate of extinction (e.g., Hodges and Arnold 1995). (Hereafter, ''diversification'' will be used to convey the net change in clade diversity as a result of speciation and extinction.) Changes in the rate of lineage diversification are expected to leave an imprint in the phylogeny of the affected species, underscoring the importance of historical inference in studies of evolutionary innovations.Key innovation hypotheses are most frequently studied by correlation, which measures the degree to which multiple origins of a trait are associated with clades of unusual size. Correlations are appealing because they use phylogenetically independent datapoints, typically comparisons of sister clades, to demonstrate a general, repeatable effect on diversity by the candidate trait. Increasing the sample size in this way reduces the potential impact of other factors that affect diversification rate. A popular approach has been to compare the relative sizes of sister groups using nonparametric sign tests, for example, in studies of insect phytophagy (Mitter et al. 1988), plant latex and resin canals (Farrell et al. 1991), floral nectar spurs (Hodges 1997), and flower symmetry (Sargent 2004). Alternatively, parametric approaches make it possible to identify unusually large clades by fitting data to predictions of null models of stochastic cladogenesis, and this can increase the statistical power associated with trait correlations (Slowinski and Guyer 1993;Sims and McConway 2003;McConway and Sims 2004).I...

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Accommodating Phylogenetic Uncertainty in Evolutionary Studies

Cited by 306 publications

References 14 publications

A phylogenetic approach to cultural evolution

A phylogenetic approach to cultural evolution

Controlling for non-independence in comparative analysis of patterns across populations within species

Detecting the Historical Signature of Key Innovations Using Stochastic Models of Character Evolution and Cladogenesis

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