Probabilistic Phylogenetic Inference in the Fossil Record: Current and Future Applications

Wagner, Peter J.; Marcot, Jonathan D.

doi:10.1017/s108933260000187x

Cited by 16 publications

(25 citation statements)

References 90 publications

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“…These two components can be changed or replaced independently of each other, for example using a different distribution to weight node age selection within the zipper model. The density function for D may be applicable with other time-scaling methods, such as Bayesian MCMC methods, which consider the likelihood of all the node ages simultaneously, although such a Bayesian framework for fossil phylogenetics is not yet fully developed (but see Wagner & Marcot 2010). Current Bayesian methods do not account for sampling intensity in the fossil record nor can place observed taxa in ancestor-descendant relationships (Ronquist et al 2012), although the probabilistic modelling described here may provide a basis for such implementation.…”

Section: Discussionmentioning

confidence: 99%

“…Current Bayesian methods do not account for sampling intensity in the fossil record nor can place observed taxa in ancestor-descendant relationships (Ronquist et al 2012), although the probabilistic modelling described here may provide a basis for such implementation. Once fully developed, Bayesian frameworks would allow for model-based phylogenetic inference methods that simultaneously estimate relationships and temporal history, allowing nodes to be time-scaled simultaneously and informing the assignment of ancestral relationships with character data (Wagner & Marcot 2010).…”

Section: Discussionmentioning

confidence: 99%

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A stochastic rate‐calibrated method for time‐scaling phylogenies of fossil taxa

2013

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Summary1. Applying phylogeny-based analyses of trait evolution and diversification in the fossil record generally involves transforming an unscaled cladogram into a phylogeny scaled to geologic time. Current methods produce single time-scaled phylogenies with no indication of the uncertainty in the temporal relationships and, under some methods, artificial zero-length branches. 2.Here, I present a stochastic algorithm for time-scaling phylogenies of fossil taxa by randomly sampling node ages from a constrained distribution, with the ultimate goal of producing large samples of time-scaled phylogenies for a given data set as the basis for phylogeny-based analyses. I describe how this stochastic approach can be extended to consider potential ancestral relationships and resolve polytomies. 3. The stochastic selection of node ages in this algorithm is weighted by the probability density of the total inferable unobserved evolutionary history at single divergence events in a tree, a distribution dependent on rates of branching, extinction and sampling in the fossil record. 4. The combined time-scaling method must be calibrated with explicit estimates of three rates: branching, extinction and sampling, and thus is named the cal3 time-scaling method, included in the R library paleotree. I test the time-scaling capabilities of the cal3 and older time-scaling methods in simulations. cal3 produces samples of time-scaled trees that better bracket the uncertainty in the true node ages than existing time-scaling methods. This is true even in simulations under a 'terminal-taxon' model of differentiation that violates many of the assumptions of the cal3 method. 5. The cal3 method provides a new approach for time-scaling palaeontological cladograms, calibrated to estimated sampling and diversification rates, allowing for better estimates of uncertainty in the phylogenetic timescaling. The cal3 method is robust to relaxation of at least some model assumptions. Additional work is needed to analyse the impact of time-scaling approaches on macroevolutionary analyses and to integrate time-scaling with phylogenetic inference.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A stochastic rate‐calibrated method for time‐scaling phylogenies of fossil taxa

2013

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“…The stratigraphic likelihood of any branch duration is the probability of zero finds over X million years based on per‐collection sampling rates illustrated in Figs and collections per‐million years illustrated in Fig. :

\begin{matrix} L [Divergence | Stratigraphy] \\ = \sum_{A = 1}^{Areas} (P false[A false] \times Π_{s = italicst}^{italicST} \sum_{q = 1}^{4} (\frac{{(1 - {Rm}_{A • s • q})}^{t}}{4})) \end{matrix}

where A is a possible ancestral region, st and ST are the first and last stages (or other chronostratigraphic units) over which a branch spans, t is the duration within that stage that the branch spans, and q is one of four quartiles within the lognormal distribution (Wagner & Marcot ). Thus, Rm A•s•q gives the per‐million‐year sampling rate for Area A in stage s from lognormal quartile q, and (1 − Rm A•s•q ) t gives the probability of zero finds over t million years.…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, if there are central tendencies to those processes that are associated with particular taxa, then we expect lognormal distributions (Montroll & Shlesinger ). Wagner & Marcot () show that sampling rates among some Ordovician–Silurian gastropods follow a lognormal distribution.…”

Section: Methodsmentioning

confidence: 99%

“…where A is a possible ancestral region, st and ST are the first and last stages (or other chronostratigraphic units) over which a branch spans, t is the duration within that stage that the branch spans, and q is one of four quartiles within the lognormal distribution (Wagner & Marcot 2010 We then estimate divergence times and branch durations under five different models. In reverse order of complexity, these are Model 1: Separate regional lognormal sampling rates from Fig.…”

Section: A P P L I C a T I O N O F R E S U L T S : D I V E R G E N C mentioning

confidence: 99%

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Modelling distributions of fossil sampling rates over time, space and taxa: assessment and implications for macroevolutionary studies

Wagner

Marcot

2013

Methods Ecol Evol

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Summary1. Observed patterns in the fossil record reflect not just macroevolutionary dynamics, but preservation patterns. Sampling rates themselves vary not simply over time or among major taxonomic groups, but within time intervals over geography and environment, and among species within clades. Large databases of presences of taxa in fossil-bearing collections allow us to quantify variation in per-collection sampling rates among species within a clade. We do this separately not just for different time/stratigraphic intervals, but also for different geographic or ecologic units within time/stratigraphic intervals. We then re-assess per-million-year sampling rates given the distributions of per-collection sampling rates 2. We use simple distribution models (geometric and lognormal) to assess general models of per-locality sampling rate distributions given occurrences among appropriate fossiliferous localities. We break these down not simply by time period, but by general biogeographic units in order to accommodate variation over space as well as among species. 3. We apply these methods to occurrence data for Meso-Cenozoic mammals drawn from the Paleobiology Database and the New and Old Worlds fossil mammal database. We find that all models of distributed rates do vastly better than the best uniform sampling rates and that the lognormal in particular does an excellent job of summarizing sampling rates. We also show that the lognormal distributions vary fairly substantially among biogeographic units of the same age. 4. As an example of the utility of these rates, we assess the most likely divergence times for basal (Eocene-Oligocene) carnivoramorphan mammals from North America and Eurasia using both stratigraphic and morphological data. The results allow for unsampled taxa or unsampled portions of sampled lineages to be in either continent and also allow for the variation in sampling rates among species. We contrast five models using stratigraphic likelihoods in different ways to summarize how they might affect macroevolutionary inferences.

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Phylogeny and Stratigraphy Comparison

Wagner

2011

Encyclopedia of Life Sciences

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Phylogenetic relationships among taxa can be estimated in a number of ways. Given known fossil records, phylogenetic estimates implicitly hypothesise the latest possible origination times of taxa. One can use these inferences as models to evaluate the quality of the fossil record. Originally, it was hoped that these metrics would obviate the need for probabilistic models. However, subsequent research shows that all these metrics are affected by the same factors affecting probabilistic models while also being highly sensitive to the accuracy of the model phylogeny. Alternatively, one can treat these inferences as hypotheses to be tested by stratigraphic distribution data. Given the sensitivity of phylogenetic inference to ideas about character evolution and levels of convergence, this offers a promising avenue for reducing untested assumptions yielding initial phylogenetic estimates. Key Concepts: Particular ideas about phylogenetic relationships have implications about both macroevolutionary models and preservation models. There are several ways to describe gaps implied by inferred phylogenies, but all reflect factors other than simple preservation rate, especially the accuracy of the phylogeny. Using stratigraphic data to test phylogenetic inferences as hypotheses might help assess the assumptions underlying phylogenetic inference.

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Probabilistic Phylogenetic Inference in the Fossil Record: Current and Future Applications

Cited by 16 publications

References 90 publications

A stochastic rate‐calibrated method for time‐scaling phylogenies of fossil taxa

A stochastic rate‐calibrated method for time‐scaling phylogenies of fossil taxa

Modelling distributions of fossil sampling rates over time, space and taxa: assessment and implications for macroevolutionary studies

Phylogeny and Stratigraphy Comparison

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