"We consider the evolution of large but finite populations on arbitrary fitness landscapes. We describe the evolutionary process by a Markov, Moran process. We show that to $mathcal O(1/N)$, the time-averaged fitness is lower for the finite population than it is for the infinite population. We also show that fluctuations in the number of individuals for a given genotype can be proportional to a power of the inverse of the mutation rate. Finally, we show that the probability for the system to take a given path through the fitness landscape can be non-monotonic in system size."

"Virtually all aspects of human behavior show enormous variation both within and between cultural groups, including material culture, social organization and language. Thousands of distinct cultural groups exist: about 6,000 languages are spoken today, and it is thought that a far greater number of languages existed in the past but became extinct. Using a Darwinian approach, this book seeks to explain this rich cultural variation. There are a number of theoretical reasons to believe that cultural diversification might be tree-like, that is phylogenetic: material and non-material culture is clearly inherited by descendants, there is descent with modification, and languages appear to be hierarchically related. There are also a number of theoretical reasons to believe that cultural evolution is not tree-like: cultural inheritance is not Mendelian and can indeed be vertical, horizontal or oblique, evidence of borrowing abounds, cultures are not necessarily biological populations and can be transient and complex. Here, for the first time, this title tackles these questions of cultural evolution empirically and quantitatively, using a range of case studies from Africa, the Pacific, Europe, Asia and America. A range of powerful theoretical tools developed in evolutionary biology is used to test detailed hypotheses about historical patterns and adaptive functions in cultural evolution. Evidence is amassed from archaeological, linguist and cultural datasets, from both recent and historical or pre-historical time periods. A unifying theme is that the phylogenetic approach is a useful and powerful framework, both for describing the evolutionary history of these traits, and also for testing adaptive hypotheses about their evolution and co-evolution. Contributors include archaeologists, anthropologists, evolutionary biologists and linguists, and this book will be of great interest to all those involved in these areas."

"The evolutionary history of species is traditionally represented using a rooted phylogenetic tree. However, when reticulate events such as hybridization, horizontal gene transfer or recombination are believed to be involved, phylogenetic networks that can accommodate non-treelike evolution have an important role to play. This book provides the first interdisciplinary overview of phylogenetic networks. Beginning with a concise introduction to both phylogenetic trees and phylogenetic networks, the fundamental concepts and results are then presented for both rooted and unrooted phylogenetic networks. Current approaches and algorithms available for computing phylogenetic networks from different types of datasets are then discussed, accompanied by examples of their application to real biological datasets. The book also summarises the algorithms used for drawing phylogenetic networks, along with the existing software for their computation and evaluation. All datasets, examples and other additional information and links are available from the book's companion website at www.phylogenetic-networks.org."

Massively-hyped paper trying to model language history using lightly-repurposed biological models comprehensively debunked by very careful and linguistically-informed data analysis. One of the authors of the debunking shows up in the comments, and says:
"Finally, regarding press; a few news organisations were interested in the initial pitch, but lost interest when they realised that we didn't have a good story here about human origins."

"Multicellularity was one of the most significant innovations in the history of life, but its initial evolution remains poorly understood. Using experimental evolution, we show that key steps in this transition could have occurred quickly. We subjected the unicellular yeast Saccharomyces cerevisiae to an environment in which we expected multicellularity to be adaptive. We observed the rapid evolution of clustering genotypes that display a novel multicellular life history characterized by reproduction via multicellular propagules, a juvenile phase, and determinate growth. The multicellular clusters are uniclonal, minimizing within-cluster genetic conflicts of interest. Simple among-cell division of labor rapidly evolved. Early multicellular strains were composed of physiologically similar cells, but these subsequently evolved higher rates of programmed cell death (apoptosis), an adaptation that increases propagule production. These results show that key aspects of multicellular complexity, a subject of central importance to biology, can readily evolve from unicellular eukaryotes."

Wow, if this holds up.

"We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks."

"Many cellular processes are carried out by molecular ‘machines’—assemblies of multiple differentiated proteins that physically interact to execute biological functions1, 2, 3, 4, 5, 6, 7, 8. Despite much speculation, strong evidence of the mechanisms by which these assemblies evolved is lacking. Here we use ancestral gene resurrection9, 10, 11 and manipulative genetic experiments to determine how the complexity of an essential molecular machine—the hexameric transmembrane ring of the eukaryotic V-ATPase proton pump—increased hundreds of millions of years ago. We show that the ring of Fungi, which is composed of three paralogous proteins, evolved from a more ancient two-paralogue complex because of a gene duplication that was followed by loss in each daughter copy of specific interfaces by which it interacts with other ring proteins. These losses were complementary, so both copies became obligate components with restricted spatial roles in the complex. Reintroducing a single historical mutation from each paralogue lineage into the resurrected ancestral proteins is sufficient to recapitulate their asymmetric degeneration and trigger the requirement for the more elaborate three-component ring. Our experiments show that increased complexity in an essential molecular machine evolved because of simple, high-probability evolutionary processes, without the apparent evolution of novel functions. They point to a plausible mechanism for the evolution of complexity in other multi-paralogue protein complexes."

I had no idea.

Cool! "Most complex multicellular organisms develop clonally from a single cell. This should limit conflicts between cell lineages that could threaten the extensive cooperation of cells within multicellular bodies. Cellular composition can be manipulated in the social amoeba Dictyostelium discoideum, which allows us to test and confirm the two key predictions of this theory. Experimental evolution at low relatedness favored cheating mutants that could destroy multicellular development. However, under high relatedness, the forces of mutation and within-individual selection are too small for these destructive cheaters to spread, as shown by a mutation accumulation experiment. Thus, we conclude that the single-cell bottleneck is a powerful stabilizer of cellular cooperation in multicellular organisms."

Although much attention has been focused on explaining and describing the diversity of social grouping patterns among primates1, 2, 3, less effort has been devoted to understanding the evolutionary history of social living4. This is partly because social behaviours do not fossilize, making it difficult to infer changes over evolutionary time. However, primate social behaviour shows strong evidence for phylogenetic inertia, permitting the use of Bayesian comparative methods to infer changes in social behaviour through time, thereby allowing us to evaluate alternative models of social evolution. Here we present a model of primate social evolution, whereby sociality progresses from solitary foraging individuals directly to large multi-male/multi-female aggregations (approximately 52 million years (Myr) ago), with pair-living (approximately 16 Myr ago) or single-male harem systems (approximately 16 Myr ago) derivative from this second stage. This model fits the data significantly better than the two widely accepted alternatives (an unstructured model implied by the socioecological hypothesis or a model that allows linear stepwise changes in social complexity through time). We also find strong support for the co-evolution of social living with a change from nocturnal to diurnal activity patterns, but not with sex-biased dispersal. This supports suggestions that social living may arise because of increased predation risk associated with diurnal activity. Sociality based on loose aggregation is followed by a second shift to stable or bonded groups. This structuring facilitates the evolution of cooperative behaviours5 and may provide the scaffold for other distinctive anthropoid traits including coalition formation, cooperative resource defence and large brains.

"Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods."

"We tend to see history and evolution springing from separate roots, one grounded in the human world and the other in the natural world. Human beings have, however, become probably the most powerful species shaping evolution today, and human-caused evolution in other species has probably been the most important force shaping human history. This book introduces readers to evolutionary history, a new field that unites history and biology to create a fuller understanding of the past than either can produce on its own. Evolutionary history can stimulate surprising new hypotheses for any field of history and evolutionary biology. How many art historians would have guessed that sculpture encouraged the evolution of tuskless elephants? How many biologists would have predicted that human poverty would accelerate animal evolution? How many military historians would have suspected that plant evolution would convert a counter-insurgency strategy into a rebel subsidy? ..."

"A model of “ephemeral” population structure is pre- sented that applies not only to biological systems in which discrete groups form but also to networks without group boundaries. The evolution of altruistic behaviors is discussed. Nonrandom interaction and nonlinear fitness structures are modeled; together, these factors can produce stable polymorphisms of altruistic and selfish types, as well as bistability. Empirical applications of the model may be found in microbes, marine invertebrates, annual plants, and other organisms."

"The strength of selection in populations has traditionally been inferred by measuring changes in bulk population parameters, such as mean reproductive rates. Untangling the effect of selection from other factors, such as specific responses to environmental fluctuations, poses a significant problem... where selection occurs within phenotypically heterogeneous populations... Using “individual histories”—temporal sequences of all reproduction events and phenotypic changes of individuals and their ancestors—we ... [quantify] selection in diverse experimental settings. Selection ... acts on histories, and a measure of selection that employs the distribution of histories is introduced."

Do go through to read the pieces by Coyne in full, they are very good, and quite merciless. (I am proud to say that the issue of depression massively lowering fitness occurred to me immediately as the chief problem, but I didn't blog about it.)

"Understanding under what conditions interacting populations, whether they be plants, animals, or viral particles, coexist... Both biotic interactions and environmental fluctuations ... can facilitate or disrupt coexistence. To better understand this interplay between these deterministic and stochastic forces, we develop a mathematical theory extending the nonlinear theory of permanence for deterministic systems to stochastic difference and differential equations. Our condition for coexistence requires that there is a fixed set of weights associated with the interacting populations and this weighted combination of populations' invasion rates is positive for any (ergodic) stationary distribution associated with a subcollection of populations. ... with] sufficient noise ... the populations approach a unique positive stationary distribution. ... coexistence criterion is robust to small perturbations of the model functions."

Huh?

How termites evolved from cockroaches. Do not read while consuming food.

"We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains - parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource."

Review of Fodor & Piatelli-Palmarini.

"A hitherto neglected form of explanation is explored, especially its role in population genetics. “Statistically abstractive explanation” (SA explanation) mandates the suppression of factors probabilistically relevant to an explanandum when these factors are extraneous to the theoretical project being pursued. When these factors are suppressed, the explanandum is rendered uncertain. But this uncertainty traces to the theoretically constrained character of SA explanation, not to any real indeterminacy. Random genetic drift is an artifact of such uncertainty, and it is therefore wrong to reify it as a cause of evolution or as a process in its own right."

Fantastic-sounding class from Christos Papadimitrou.

Sounds absolutely fascinating.

The first edition was really good, so I look forward to the new one.

This looks absolutely fascinating, but that price is _not_ the result of a slipped decimal point.

It sounds like a bunch of warblogger scientists.

"We present a method to quantify the topological distance between two networks of different sizes"

Worth stealing^H^H^H borrowing from?

With bonus William Saletan bashing!

Lenski's second letter is a delight. (Is this Schlafly any relation of Phyllis?)

Controversy with Jerry Fodor over the latter's (bizarre) views about evolution.

"when hospitals actively hunt for carriers of MRSA and beef up precautions to prevent its spread, they can dramatically reduce serious infections and deaths. But despite 30 years of research showing that these “search and destroy” tactics can stop MRS

Neat, for an evolutionary comparative biochemistry value of neat.

"focuses on exciting new developments in long-term analyses of animal populations where pedigree information has been collected". Apparently free access.

Worth reading for the nasalization graph alone.