Daniel T. Ksepka

Our ability to correlate biological evolution with climate change, geological evolution, and other historical patterns is essential to understanding the processes that shape biodiversity. Combining data from the fossil record with molecular phylogenetics represents an exciting synthetic approach to this challenge. The first molecular divergence dating analysis (Zuckerkandl and Pauling 1962) was based on a measure of the amino acid differences in the hemoglobin molecule, with replacement rates established (calibrated) using paleontological age estimates from textbooks (e.g., Dodson 1960). Since that time, the amount of molecular sequence data has increased dramatically, affording ever-greater opportunities to apply molecular divergence approaches to fundamental problems in evolutionary biology.

Avian diversification has been influenced by global climate change, plate tectonic movements, and mass extinction events. However, the impact of these factors on the diversification of the hyperdiverse perching birds (passerines) is unclear because family level relationships are unresolved and the timing of splitting events among lineages is uncertain. We analyzed DNA data from 4,060 nuclear loci and 137 passerine families using concatenation and coalescent approaches to infer a comprehensive phylogenetic hypothesis that clarifies relationships among all passerine families. Then, we calibrated this phylogeny using 13 fossils to examine the effects of different events in Earth history on the timing and rate of passerine diversification. Our analyses reconcile passerine diversification with the fossil and geological records; suggest that passerines originated on the Australian landmass ∼47 Ma; and show that subsequent dispersal and diversification of passerines was affected by a number of climatological and geological events, such as Oligocene glaciation and inundation of the New Zealand landmass. Although passerine diversification rates fluctuated throughout the Cenozoic, we find no link between the rate of passerine diversification and Cenozoic global temperature, and our analyses show that the increases in passerine diversification rate we observe are disconnected from the colonization of new continents. Taken together, these results suggest more complex mechanisms than temperature change or ecological opportunity have controlled macroscale patterns of passerine speciation.

The total-evidence approach to divergence time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework. Current model-based implementations of this approach lack an appropriate model for the tree describing the diversification and fossilization process and can produce estimates that lead to erroneous conclusions. We address this shortcoming by providing a total-evidence method implemented in a Bayesian framework. This approach uses a mechanistic tree prior to describe the underlying diversification process that generated the tree of extant and fossil taxa. Previous attempts to apply the total-evidence approach have used tree priors that do not account for the possibility that fossil samples may be direct ancestors of other samples, that is, ancestors of fossil or extant species or of clades. The fossilized birth–death (FBD) process explicitly models the diversification, fossilization, and sampling processes and naturally allows for sampled ancestors. This model was recently applied to estimate divergence times based on molecular data and fossil occurrence dates. We incorporate the FBD model and a model of morphological trait evolution into a Bayesian total-evidence approach to dating species phylogenies. We apply this method to extant and fossil penguins and show that the modern penguins radiated much more recently than has been previously estimated, with the basal divergence in the crown clade occurring at ∼12.7 ∼12.7 Ma and most splits leading to extant species occurring in the last 2 myr. Our results demonstrate that including stem-fossil diversity can greatly improve the estimates of the divergence times of crown taxa. The method is available in BEAST2 (version 2.4) software www.beast2.org with packages SA (version at least 1.1.4) and morph-models (version at least 1.0.4) installed.

Penguin feathers are highly modified in form and function, but there have been no fossils to inform their evolution. A giant penguin with feathers was recovered from the late Eocene (~36 million years ago) of Peru. The fossil reveals that key feathering features, including undifferentiated primary wing feathers and broad body contour feather shafts, evolved early in the penguin lineage. Analyses of fossilized color-imparting melanosomes reveal that their dimensions were similar to those of non-penguin avian taxa and that the feathering may have been predominantly gray and reddish-brown. In contrast, the dark black-brown color of extant penguin feathers is generated by large, ellipsoidal melanosomes previously unknown for birds. The nanostructure of penguin feathers was thus modified after earlier macrostructural modifications of feather shape linked to aquatic flight.

Relative brain size has long been considered a reflection of cognitive capacities and has played a fundamental role in developing core theories in the life sciences. Yet, the notion that relative brain size validly represents selection on brain size relies on the untested assumptions that brain-body allometry is restrained to a stable scaling relationship across species and that any deviation from this slope is due to selection on brain size. Using the largest fossil and extant dataset yet assembled, we find that shifts in allometric slope underpin major transitions in mammalian evolution and are often primarily characterized by marked changes in body size. Our results reveal that the largest-brained mammals achieved large relative brain sizes by highly divergent paths. These findings prompt a reevaluation of the traditional paradigm of relative brain size and open new opportunities to improve our understanding of the genetic and developmental mechanisms that influence brain size.