Solving the prisoner's dilemma trap in Hamilton's model of temporarily formed random groups.

Explaining the evolution of cooperation in the strong altruism scenario, where a cooperator does not benefit from her contribution to the public goods, is a challenging problem that requires positive assortment among cooperators (i.e., cooperators must tend to associate with other cooperators) or punishment of defectors. The need for these drastic measures stems from the analysis of a group selection model of temporarily formed random groups introduced by Hamilton nearly fifty years ago to describe the fate of altruistic behavior in a population. Challenging conventional wisdom, we show analytically here that strong altruism evolves in Hamilton's original model in the case of biparental sexual reproduction. Moreover, when the cost of cooperation is small and the amplified contribution shared by group members is large, cooperation is the only stable strategy in equilibrium. Thus, our results provide a solution to the 'problem of origination' of strong altruism, i.e. how cooperation can take off from an initial low frequency of cooperators. We discuss a possible reassessment of cooperation in cases of viral co-infection, as cooperation may even be favored in situations where the prisoner's dilemma applies.

A novel block-coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling.

Joint models for longitudinal and time-to-event data are receiving increasing attention owing to its capability of capturing the possible association between these two types of data. Typically, a joint model consists of a longitudinal submodel for longitudinal processes and a survival submodel for the time-to-event response, and links two submodels by common covariates that may carry both fixed and random effects. However, research gaps still remain on how to simultaneously select fixed and random effects from the two submodels under the joint modeling framework efficiently and effectively. In this article, we propose a novel block-coordinate gradient descent (BCGD) algorithm to simultaneously select multiple longitudinal covariates that may carry fixed and random effects in the joint model. Specifically, for the multiple longitudinal processes, a linear mixed effect model is adopted where random intercepts and slopes serve as essential covariates of the trajectories, and for the survival submodel, the popular proportional hazard model is employed. A penalized likelihood estimation is used to control the dimensionality of covariates in the joint model and estimate the unknown parameters, especially when estimating the covariance matrix of random effects. The proposed BCGD method can successfully capture the useful covariates of both fixed and random effects with excellent selection power, and efficiently provide a relatively accurate estimate of fixed and random effects empirically. The simulation results show excellent performance of the proposed method and support its effectiveness. The proposed BCGD method is further applied on two real data sets, and we examine the risk factors for the effects of different heart valves, differing on type of tissue, implanted in the aortic position and the risk factors for the diagnosis of primary biliary cholangitis.

The complicated legacy of E. O. Wilson with respect to genetics and human behavior.

Over the arc of his career, E. O. Wilson first embraced, then popularized, and finally rejected an extreme genetical hereditarian view of human nature. The controversy that ensued during the period of popularization (largely in the 1970s and 1980s) obscured the fact that empirical and theoretical research during this time undercut the assumptions necessary for this view. By the end of his career, Wilson accepted the fact that individual/kin selection models were insufficient to explain human behavior and society, and he began conducting research based upon multilevel (group) selection, an idea he had previously scorned.

Evolutionary branching in multi-level selection models.

We study a model of group-structured populations featuring individual-level birth and death events, and group-level fission and extinction events. Individuals play games within their groups, while groups play games against other groups. Payoffs from individual-level games affect birth rates of individuals, and payoffs from group-level games affect group extinction rates. We focus on the evolutionary dynamics of continuous traits with particular emphasis on the phenomenon of evolutionary diversification. Specifically, we consider two-level processes in which individuals and groups play continuous snowdrift or prisoner's dilemma games. Individual game strategies evolve due to selection pressure from both the individual and group level interactions. The resulting evolutionary dynamics turns out to be very complex, including branching and type-diversification at one level or the other. We observe that a weaker selection pressure at the individual level results in more adaptable groups and sometimes group-level branching. Stronger individual-level selection leads to more effective adaptation within each group while preventing the groups from adapting according to the group-level games.

Intra-colony venom diversity contributes to maintaining eusociality in a cooperatively breeding ant.

BACKGROUND: Eusociality is widely considered to evolve through kin selection, where the reproductive success of an individual's close relative is favored at the expense of its own. High genetic relatedness is thus considered a prerequisite for eusociality. While ants are textbook examples of eusocial animals, not all ants form colonies of closely related individuals. One such example is the ectatommine ant Rhytidoponera metallica, which predominantly forms queen-less colonies that have such a low intra-colony relatedness that they have been proposed to represent a transient, unstable form of eusociality. However, R. metallica is among the most abundant and widespread ants on the Australian continent. This apparent contradiction provides an example of how inclusive fitness may not by itself explain the maintenance of eusociality and raises the question of what other selective advantages maintain the eusocial lifestyle of this species. RESULTS: We provide a comprehensive portrait of the venom of R. metallica and show that the colony-wide venom consists of an exceptionally high diversity of functionally distinct toxins for an ant. These toxins have evolved under strong positive selection, which is normally expected to reduce genetic variance. Yet, R. metallica exhibits remarkable intra-colony variation, with workers sharing only a relatively small proportion of toxins in their venoms. This variation is not due to the presence of chemical castes, but has a genetic foundation that is at least in part explained by toxin allelic diversity. CONCLUSIONS: Taken together, our results suggest that the toxin diversity contained in R. metallica colonies may be maintained by a form of group selection that selects for colonies that can exploit more resources and defend against a wider range of predators. We propose that increased intra-colony genetic variance resulting from low kinship may itself provide a selective advantage in the form of an expanded pharmacological venom repertoire. These findings provide an example of how group selection on adaptive phenotypes may contribute to maintaining eusociality where a prerequisite for kin selection is diminished.

Sparse group variable selection for gene-environment interactions in the longitudinal study.

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G × $\times $ E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G × $\times $ E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Robust Bayesian variable selection for gene-environment interactions.

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes.

Artificial selection of microbiota opens new avenues for improving plants. However, reported results lack consistency. We hypothesised that the success in artificial selection of microbiota depends on the stabilisation of community structure. In a ten-generation experiment involving 1,800 plants, we selected rhizosphere microbiota of Brachypodium distachyon associated with high or low leaf greenness, a proxy of plant performance. The microbiota structure showed strong fluctuations during an initial transitory phase, with no detectable leaf greenness heritability. After five generations, the microbiota structure stabilised, concomitantly with heritability in leaf greenness. Selection, initially ineffective, did successfully alter the selected property as intended, especially for high selection. We show a remarkable correlation between the variability in plant traits and selected microbiota structures, revealing two distinct sub-communities associated with high or low leaf greenness, whose abundance was significantly steered by directional selection. Understanding microbiota structure stabilisation will improve the reliability of artificial microbiota selection.

Acculturation drives the evolution of intergroup conflict.

Conflict between groups of individuals is a prevalent feature in human societies. A common theoretical explanation for intergroup conflict is that it provides benefits to individuals within groups in the form of reproduction-enhancing resources, such as food, territory, or mates. However, it is not always the case that conflict results from resource scarcity. Here, we show that intergroup conflict can evolve, despite not providing any benefits to individuals or their groups. The mechanism underlying this process is acculturation: the adoption, through coercion or imitation, of the victor's cultural traits. Acculturation acts as a cultural driver (in analogy to meiotic drivers) favoring the transmission of conflict, despite a potential cost to both the host group as a whole and to individuals in that group. We illustrate this process with a two-level model incorporating state-dependent event rates and evolving traits for both individuals and groups. Individuals can become "warriors" who specialize in intergroup conflicts, but are costly otherwise. Additionally, groups are characterized by cultural traits, such as their tendency to engage in conflict with other groups and their tendency for acculturation. We show that, if groups engage in conflicts, group selection will favor the production of warriors. Then, we show that group engagement can evolve if it is associated with acculturation. Finally, we study the coevolution of engagement and acculturation. Our model shows that horizontal transmission of culture between interacting groups can act as a cultural driver and lead to the maintenance of costly behaviors by both individuals and groups.

Organizational Development as Generative Entrenchment.

A critical task for organizations is how to best structure themselves to efficiently allocate information and resources to individuals tasked with solving sub-components of the organization's central problems. Despite this criticality, the processes by which organizational structures form remain largely opaque within organizational theory, with most approaches focused on how structure is influenced by individual managerial heuristics, normative cultural perceptions, and trial-and-error. Here, we propose that a broad understanding of organizational formation can be aided by appealing to generative entrenchment, a theory from developmental biology that helps explain why phylogenetically diverse animals appear similar as embryos. Drawing inferences from generative entrenchment and applying it to organizational differentiation, we argue that the reason many organizations appear structurally similar is due to core informational restraints on individual actors beginning at the top and descending to the bottom of these informational hierarchies, which reinforces these structures via feedback between separate levels. We further argue that such processes can lead to the emergence of a variety of group-level traits, an important but undertheorized class of phenomena in cultural evolution.

Multilevel Selection on Offspring Size and the Maintenance of Variation.

AbstractMultilevel selection on offspring size occurs when offspring fitness depends on both absolute size (hard selection) and size relative to neighbors (soft selection). We examined multilevel selection on egg size at two biological scales-within clutches and among clutches from different females-using an external fertilizing tube worm. We exposed clutches of eggs to two sperm environments (limiting and saturating) and measured their fertilization success. We then modeled environmental (sperm-dependent) differences in hard and soft selection on individual eggs as well as selection on clutch-level traits (means and variances). Hard and soft selection differed in strength and form depending on sperm availability-hard selection was consistently stabilizing; soft selection was directional and favored eggs relatively larger (sperm limitation) or smaller (sperm saturation) than the clutch mean. At the clutch level, selection on mean egg size was largely concave, while selection on within-clutch variance was weak but generally negative-although some correlational selection occurred between these two traits. Importantly, we found that the optimal clutch mean egg size differed for mothers and offspring, suggesting some antagonism between the levels of selection. We thus identify several pathways that may maintain offspring size variation: environmentally (sperm-) dependent soft selection, antagonistic multilevel selection, and correlational selection on clutch means and variances. Multilevel approaches are powerful but seldom-used tools for studies of offspring size, and we encourage their future use.

Evaluating kin and group selection as tools for quantitative analysis of microbial data.

Kin selection and multilevel selection theory are often used to interpret experiments about the evolution of cooperation and social behaviour among microbes. But while these experiments provide rich, detailed fitness data, theory is mostly used as a conceptual heuristic. Here, we evaluate how kin and multilevel selection theory perform as quantitative analysis tools. We reanalyse published microbial datasets and show that the canonical fitness models of both theories are almost always poor fits because they use statistical regressions misspecified for the strong selection and non-additive effects we show are widespread in microbial systems. We identify analytical practices in empirical research that suggest how theory might be improved, and show that analysing both individual and group fitness outcomes helps clarify the biology of selection. A data-driven approach to theory thus shows how kin and multilevel selection both have untapped potential as tools for quantitative understanding of social evolution in all branches of life.

Cooperation in large-scale human societies-What, if anything, makes it unique, and how did it evolve?

To resolve the major controversy about why prosocial behaviors persist in large-scale human societies, we propose that two questions need to be answered. First, how do social interactions in small-scale and large-scale societies differ? By reviewing the exchange and collective-action dilemmas in both small-scale and large-scale societies, we show they are not different. Second, are individual decision-making mechanisms driven by self-interest? We extract from the literature three types of individual decision-making mechanism, which differ in their social influence and sensitivity to self-interest, to conclude that humans interacting with non-relatives are largely driven by self-interest. We then ask: what was the key mechanism that allowed prosocial behaviors to continue as societies grew? We show the key role played by new social interaction mechanisms-change in the rules of exchange and collective-action dilemmas-devised by the interacting individuals, which allow for self-interested individuals to remain prosocial as societies grow.

A Note Against the Use of "Belonging To" Properties in Multilevel Selection Theory.

In this short paper, I argue against what I call the "belonging to" interpretation of group selection in scenarios in which a group's fitness is defined as the per capita reproductive output of the individuals of the group. According to this interpretation, group selection acts on "belonging to" properties of individuals, i.e. on relational or contextual properties that all the individuals of a group share simply by belonging to that group; thus, if differences in the individuals' "belonging to" properties cause differences in their fitness, group selection sensu the "belonging to" interpretation is said to be at work. I argue that the main problem with the "belonging to" interpretation is that it confuses evolutionary changes due to differences in environmental quality with evolutionary changes due to selection. In other words, I argue that, in the majority of cases, this interpretation actually takes the "selection" out of the "group selection" notion it aims to interpret: by adopting this perspective, one implicitly commits to explaining the evolutionary change under consideration not by a kind of selection (be it individual or group selection), but by differences in the environmental quality experienced by individual types.

Is Wilson's religion Durkheim's, or Hobbes's Leviathan?

This paper critically supports the modern evolutionary explanation of religion popularised by David Sloan Wilson, by comparing it with those of his predecessors, namely Emile Durkheim and Thomas Hobbes, and to some biological examples which seem analogous to religions as kinds of superorganisms in their own right. The aim of the paper is to draw out a theoretical pedigree in philosophy and sociology that is reflected down the lines of various other evolutionarily minded contributors on the subject of religion. The general theme is of evolved large-scale cooperative structures. A scholarly concern is as follows: Wilson (Darwin's Cathedral: Evolution, Religion, And The Nature Of Society, University of Chicago Press, Chicago, 2002) draws on Durkheim, (The elementary forms of religious life. Free Press, New york, 1912) using Calvinism as an example without mentioning Hobbes (Leviathan, Edited by E. Curley, Cambridge, Hackett, 1651), but it was Thomas Hobbes (1588-1679) who used Calvinism as an example of a leviathanesque religious structure-which is not acknowledged by either Wilson or Durkheim. If there are even any similarities between these authors, there appears to be an omission somewhere which should rightly be accounted for by giving credit to Hobbes where it is due. I issue on conclusion, what it is that makes Wilson's approach radically different to that it skates on. I also issue it with a cautionary word.

Meta-population structure and the evolutionary transition to multicellularity.

The evolutionary transition to multicellularity has occurred on numerous occasions, but transitions to complex life forms are rare. Here, using experimental bacterial populations as proxies for nascent multicellular organisms, we manipulate ecological factors shaping the evolution of groups. Groups were propagated under regimes requiring reproduction via a life cycle replete with developmental and dispersal (propagule) phases, but in one treatment lineages never mixed, whereas in a second treatment, cells from different lineages experienced intense competition during the dispersal phase. The latter treatment favoured traits promoting cell growth at the expense of traits underlying group fitness - a finding that is supported by results from a mathematical model. Our results show that the transition to multicellularity benefits from ecological conditions that maintain discreteness not just of the group (soma) phase, but also of the dispersal (germline) phase.

Bi-level feature selection in high dimensional AFT models with applications to a genomic study.

We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.

Explanatory goals and explanatory means in multilevel selection theory.

It has become customary in multilevel selection theory to use the same terms (namely "multilevel selection 1" and "multilevel selection 2") to denote both two explanatory goals (explaining why certain individual- and, respectively, group-level traits spread) and two explanatory means (namely, two kinds of group selection we may appeal to in such explanations). This paper spells out some of the benefits that derive from avoiding this terminological conflation. I argue that keeping explanatory means and goals well apart allows us to see that, contrary to a popular recent idea, Price's equation and contextual analysis-the statistical methods most extensively used for measuring the effects of certain evolutionary factors (like individual selection, group selection etc.) on the change in the focal individual trait in multilevel selection scenarios-do not come with built-in notions of group selection and, therefore, the efficacy of these methods at analyzing various kinds of cases does not constitute a basis for deciding how group selection should best be defined. Moreover, contrary to another widely accepted idea, I argue that more than one type of group selection may serve as explanatory means when one's goal is that of explaining the evolution of individual traits in multilevel selection scenarios and I spell out how this explanatory role should be understood.

Integrative multi-view regression: Bridging group-sparse and low-rank models.

Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. We cast this new problem under the familiar multivariate regression framework and propose an integrative reduced-rank regression (iRRR), where each view has its own low-rank coefficient matrix. As such, latent features are extracted from each view in a supervised fashion. For model estimation, we develop a convex composite nuclear norm penalization approach, which admits an efficient algorithm via alternating direction method of multipliers. Extensions to non-Gaussian and incomplete data are discussed. Theoretically, we derive non-asymptotic oracle bounds of iRRR under a restricted eigenvalue condition. Our results recover oracle bounds of several special cases of iRRR including Lasso, group Lasso, and nuclear norm penalized regression. Therefore, iRRR seamlessly bridges group-sparse and low-rank methods and can achieve substantially faster convergence rate under realistic settings of multi-view learning. Simulation studies and an application in the Longitudinal Studies of Aging further showcase the efficacy of the proposed methods.

Natural selection in compartmentalized environment with reshuffling.

The emerging field of high-throughput compartmentalized in vitro evolution is a promising new approach to protein engineering. In these experiments, libraries of mutant genotypes are randomly distributed and expressed in microscopic compartments-droplets of an emulsion. The selection of desirable variants is performed according to the phenotype of each compartment. The random partitioning leads to a fraction of compartments receiving more than one genotype making the whole process a lab implementation of the group selection. From a practical point of view (where efficient selection is typically sought), it is important to know the impact of the increase in the mean occupancy of compartments on the selection efficiency. We carried out a theoretical investigation of this problem in the context of selection dynamics for an infinite non-mutating subdivided population that randomly colonizes an infinite number of patches (compartments) at each reproduction cycle. We derive here an update equation for any distribution of phenotypes and any value of the mean occupancy. Using this result, we demonstrate that, for the linear additive fitness, the best genotype is still selected regardless of the mean occupancy. Furthermore, the selection process is remarkably resilient to the presence of multiple genotypes per compartments, and slows down approximately inversely proportional to the mean occupancy at high values. We extend out results to more general expressions that cover nonadditive and non-linear fitnesses, as well non-Poissonian distribution among compartments. Our conclusions may also apply to natural genetic compartmentalized replicators, such as viruses or early trans-acting RNA replicators.