Selection constrains phenotypic evolution in a functionally important plant trait
Christopher D Muir
doi: http://dx.doi.org/10.1101/015172

A long-standing idea is that the macroevolutionary adaptive landscape — a `map’ of phenotype to fitness — constrains evolution because certain phenotypes are fit, while others are universally unfit. Such constraints should be evident in traits that, across many species, cluster around particular modal values, with few intermediates between modes. Here, I compile a new global database of 599 species from 94 plant families showing that stomatal ratio, an important functional trait affecting photosynthesis, is multimodal, hinting at distinct peaks in the adaptive landscape. The dataset confirms that most plants have all their stomata on the lower leaf surface (hypostomy), but shows for the first time that species with roughly half their stomata on each leaf surface (amphistomy) form a distinct mode in the trait distribution. Based on a new evolutionary process model, this multimodal pattern is unlikely without constraint. Further, multimodality has evolved repeatedly across disparate families, evincing long-term constraint on the adaptive landscape. A simple cost-benefit model of stomatal ratio demonstrates that selection alone is sufficient to generate an adaptive landscape with multiple peaks. Finally, phylogenetic comparative methods indicate that life history evolution drives shifts between peaks. This implies that the adaptive benefit conferred by amphistomy — increased photosynthesis — is most important in plants with fast life histories, challenging existing ideas that amphistomy is an adaptation to thick leaves and open habitats. I conclude that peaks in the adaptive landscape have been constrained by selection over much of land plant evolution, leading to predictable, repeatable patterns of evolution.

Bayesian priors for tree calibration: Evaluating two new approaches based on fossil intervals
Ryan W Norris, Cory L Strope, David M McCandlish, Arlin Stoltzfus
doi: http://dx.doi.org/10.1101/014340

Background: Studies of diversification and trait evolution increasingly rely on combining molecular sequences and fossil dates to infer time-calibrated phylogenetic trees. Available calibration software provides many options for the shape of the prior probability distribution of ages at a node to be calibrated, but the question of how to assign a Bayesian prior from limited fossil data remains open. Results: We introduce two new methods for generating priors based upon (1) the interval between the two oldest fossils in a clade, i.e., the penultimate gap (PenG), and (2) the ghost lineage length (GLin), defined as the difference between the oldest fossils for each of two sister lineages. We show that PenG and GLin/2 are point estimates of the interval between the oldest fossil and the true age for the node. Furthermore, given either of these quantities, we derive a principled prior distribution for the true age. This prior is log-logistic, and can be implemented approximately in existing software. Using simulated data, we test these new methods against some other approaches. Conclusions: When implemented as approaches for assigning Bayesian priors, the PenG and GLin methods increase the accuracy of inferred divergence times, showing considerably more precision than the other methods tested, without significantly greater bias. When implemented as approaches to post-hoc scaling of a tree by linear regression, the PenG and GLin methods exhibit less bias than other methods tested. The new methods are simple to use and can be applied to a variety of studies that call for calibrated trees.