An Adaptive Threshold in Mammalian Neocortical Evolution

Expansion of the neocortex is a hallmark of human evolution. However, it remains an open question what adaptive mechanisms facilitated its expansion. Here we show, using gyrencephaly index (GI) and other physiological and life-history data for 102 mammalian species, that gyrencephaly is an ancestral mammalian trait. We provide evidence that the evolution of a highly folded neocortex, as observed in humans, requires the traversal of a threshold of 10^9 neurons, and that species above and below the threshold exhibit a bimodal distribution of physiological and life-history traits, establishing two phenotypic groups. We identify, using discrete mathematical models, proliferative divisions of progenitors in the basal compartment of the developing neocortex as evolutionarily necessary and sufficient for generating a fourteen-fold increase in daily prenatal neuron production and thus traversal of the neuronal threshold. We demonstrate that length of neurogenic period, rather than any novel progenitor-type, is sufficient to distinguish cortical neuron number between species within the same phenotypic group.Comment: Currently under review; 38 pages, 5 Figures, 13 Supplementary Figures, 2 Table

Inferring Evolutionary Process From Neuroanatomical Data

Brain evolution has interested neuroanatomists for over a century. These interests often fall on how free the brain is to evolve independently of the body, how free brain regions are to evolve independently of each other, and how different environmental and ecological factors affect the brain over evolutionary time. But despite major advances in phylogenetic methods, comparative neuroanatomists have tended to limit their macroevolutionary toolbox to regression-based analyses and ignored the scope of evolutionary process-based models at their disposal. This Review summarizes the history of comparative neuroanatomy and highlights the pitfalls of the methodologies traditionally used. It provides an overview of evolutionary process-based modeling approaches for investigating univariate and multivariate data, as well as more sophisticated methods that incorporate hypotheses about biotic and abiotic pressures that may drive brain evolution. The benefits of evolutionary process-based models, and shortcomings of regression-based ones, are illustrated with widely used neuroanatomical data. Ultimately, the intent of this Review is to be a guide for subsuming macroevolutionary methods not typically used in comparative neuroanatomy, in order to improve our understanding of how the brain evolves

Figure_S2

Speciation values for diversification models. Birth-death trees were constructed according to one of six diversification models: increasing speciation, decreasing speciation, decreasing speciation below extinction; and constant speciation-extinction with (i) ancient mass-extinction (0.1 survival probability), (ii) recent mass-extinction (0.1 survival probability), or (iii) no mass-extinction. For all models, μ = 0.05

Supplemental Figure 4

The ability to correctly recover trees simulated under a time-dependent model or temperature-dependent model where the dependence is on extinction. Columns in each plot show the percentage of trees recovered (A,B) or Akaike weights (C,D) for constant-rate (black), time-dependent (light blue), or temperature-dependent extinction (brown) models for a set of trees simulated under (A,C) time-dependence or (B,D) temperature-dependence. Trees simulated under time-dependent extinction (B,D) were also fit with time-dependent speciation models (grey). The x-axis shows the strength of the dependencies (i.e., $\alpha$ value) used to simulate each set of trees. Akaike weights are averaged over all trees simulated with the same $\alpha$.

Bulgaria, the end of innocence

The bombing of Israeli tourists in the resort of Burgas suggests that Bulgaria's strategic choices have made it vulnerable to terrorist attack, says John O'Brennan

Data from: Characterizing and comparing phylogenies from their Laplacian spectrum

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profile of a phylogenetic tree from its Laplacian graph. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications

Supplemental Figure 11

Environment-dependency in Ruminantia computed from the Ruminantia supertree (11). AICc support for different environment-dependent models, a constant-rate birth-death model, and an exponential time-dependent model (without extinction) on a distribution of 5000 posteriorly sampled probabilities of the Ruminantia supertree. All environment-dependent models have an exponential dependency on the environmental variable. Colors correspond to Figure 1

Supplemental Figure 1

Recovered parameter estimates for trees simulated with a constant λ and an exponential dependency of μ on temperature. Simulations with: (A) varying λ0, constant μ0, and constant αμ; (B) constant λ0, varying μ0, and constant αμ; and (C) constant λ0, constant μ0, and varying αμ. Dashed red lines mark the simulated parameter value

Supplemental Figure 5

Parameter estimates for trees simulated with a positive (left) and negative (right) exponential dependency of $\lambda$ on temperature by fitting the temperature-dependent model. Parameter estimates are shown for trees with different species richness. Simulated parameters are marked by dashed red lines. Akaike weights are shown for trees fitted with temperature-dependent models (grey), time-dependent models (light blue), and constant-rate models (black). Weights are averaged across all trees within each species richness bracket

Figure_Supplemental_1

Measuring the effect of phylogenetic signal on splitter values.} (A) Boxplot of the splitter values for 100 randomized datasets (white) obtained for each of the ten datasets with two monophyletic clusters. Splitter values for the initial BM datasets with two clusters are shown in purple. Boxplot of 100 datasets simulated under a simple BM process with no clusters on a single tree (coral) is shown for comparison. (B) Boxplot of Blomberg’s $K$ for each randomized dataset (white); values for the initial BM datasets with two clusters are shown in purple. Boxplot of 100 datasets simulated under a simple BM process with no clusters on a single tree (coral) is shown for comparison