The labile brain. II. Transients, complexity and selection

The successive expression of neuronal transients is related to dynamic correlations and, as shown in this paper, to dynamic instability. Dynamic instability is a form of complexity, typical of neuronal systems, which may be crucial for adaptive brain function from two perspectives. The first is from the point of view of neuronal selection and self-organizing systems: if selective mechanisms underpin the emergence of adaptive neuronal responses then dynamic instability is, itself, necessarily adaptive. This is because dynamic instability is the source of diversity on which selection acts and is therefore subject to selective pressure. In short, the emergence of order, through selection, depends almost paradoxically on the instabilities that characterize the diversity of brain dynamics. The second perspective is provided by information theory.     

The long-term evolution of multilocus traits under frequency-dependent disruptive selection

Frequency-dependent disruptive selection is widely recognized as an important source of genetic variation. Its evolutionary consequences have been extensively studied using phenotypic evolutionary models, based on quantitative genetics, game theory, or adaptive dynamics. However, the genetic assumptions underlying these approaches are highly idealized and, even worse, predict different consequences of frequency-dependent disruptive selection. Population genetic models, by contrast, enable genotypic evolutionary models, but traditionally assume constant fitness values. Only a minority of these models thus addresses frequency-dependent selection, and only a few of these do so in a multilocus context. An inherent limitation of these remaining studies is that they only investigate the short-term maintenance of genetic variation. Consequently, the long-term evolution of multilocus characters under frequency-dependent disruptive selection remains poorly understood. We aim to bridge this gap between phenotypic and genotypic models by studying a multilocus version of Levene's soft-selection model. Individual-based simulations and deterministic approximations based on adaptive dynamics theory provide insights into the underlying evolutionary dynamics. Our analysis uncovers a general pattern of polymorphism formation and collapse, likely to apply to a wide variety of genetic systems: after convergence to a fitness minimum and the subsequent establishment of genetic polymorphism at multiple loci, genetic variation becomes increasingly concentrated on a few loci, until eventually only a single polymorphic locus remains. This evolutionary process combines features observed in quantitative genetics and adaptive dynamics models, and it can be explained as a consequence of changes in the selection regime that are inherent to frequency-dependent disruptive selection. Our findings demonstrate that the potential of frequency-dependent disruptive selection to maintain polygenic variation is considerably smaller than previously expected.     

Sexual conflict and the evolution of female preferences for indicators of male quality

Males and females have opposing interests when it comes to the honesty of signals used in mate choice. The existence of this sexual conflict has long been acknowledged, but its consequences have not been fully investigated. By applying adaptive dynamics methods and individual-based computer simulations to a standard model for good-genes sexual selection, we show that sexual conflict over condition-dependent signaling can prevent the handicap process from ever attaining an evolutionary equilibrium. We outline the parameter conditions and properties of the underlying genetics conducive to nonequilibrium behavior and discuss the potential of such behavior to explain the elaboration and frequent phylogenetic loss of sexually selected traits. We also evaluate its consequences for well-established insights of sexual selection theory previously shown to apply when female mating preference and male ornament expression do converge on stable equilibrium levels. Contrary to equilibrium expectation, a continual change of condition-dependent signaling enables the evolution of a costly preference for a pure epistatic indicator and the evolution of preferences for redundant signals or a large number of independent ornaments. We thus conclude that seemingly general results of sexual selection theory, insofar as these are based on equilibrium considerations, do not extend to cases where nonequilibrium behavior occurs.     

Environment-dependent inbreeding depression: its ecological and evolutionary significance

Inbreeding depression is a major evolutionary and ecological force that influences population dynamics and the evolution of inbreeding-avoidance traits such as mating systems and dispersal. There is now compelling evidence that inbreeding depression is environment-dependent. Here, we discuss ecological and evolutionary consequences of environment-dependent inbreeding depression. The environmental dependence of inbreeding depression may be caused by environment-dependent phenotypic expression, environment-dependent dominance, and environment-dependent natural selection. The existence of environment-dependent inbreeding depression challenges classical models of inbreeding as caused by unconditionally deleterious alleles, and suggests that balancing selection may shape inbreeding depression in natural populations; loci associated with inbreeding depression in some environments may even contribute to adaptation to others. Environment-dependent inbreeding depression also has important, often neglected, ecological and evolutionary consequences: it can influence the demography of marginal or colonizing populations and alter adaptive optima of mating systems, dispersal, and their associated traits. Incorporating the environmental dependence of inbreeding depression into theoretical models and empirical studies is necessary for understanding the genetic and ecological basis of inbreeding depression and its consequences in natural populations.     

Habitat selection and population regulation in temporally fluctuating environments

Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology, and the theory of density-dependent habitat selection (DDHS) provides ecologists with an inferential framework linking evolution and population dynamics. Current theory does not allow for temporal variation in habitat quality, a serious limitation when confronted with real ecological systems. We develop both a stochastic equivalent of the ideal free distribution to study how spatial patterns of habitat use depend on the magnitude and spatial correlation of environmental stochasticity and also a stochastic habitat selection rule. The emerging patterns are confronted with deterministic predictions based on isodar analysis, an established empirical approach to the analysis of habitat selection patterns. Our simulations highlight some consistent patterns of habitat use, indicating that it is possible to make inferences about the habitat selection process based on observed patterns of habitat use. However, isodar analysis gives results that are contingent on the magnitude and spatial correlation of environmental stochasticity. Hence, DDHS is better revealed by a measure of habitat selectivity than by empirical isodars. The detection of DDHS is but a small component of isodar theory, which remains an important conceptual framework for linking evolutionary strategies in behavior and population dynamics.     

Social plasticity in fish: integrating mechanisms and function

Social plasticity is a ubiquitous feature of animal behaviour. Animals must adjust the expression of their social behaviour to the nuances of daily social life and to the transitions between life‐history stages, and the ability to do so affects their Darwinian fitness. Here, an integrative framework is proposed for understanding the proximate mechanisms and ultimate consequences of social plasticity. According to this framework, social plasticity is achieved by rewiring or by biochemically switching nodes of the neural network underlying social behaviour in response to perceived social information. Therefore, at the molecular level, it depends on the social regulation of gene expression, so that different brain genomic and epigenetic states correspond to different behavioural responses and the switches between states are orchestrated by signalling pathways that interface the social environment and the genotype. At the evolutionary scale, social plasticity can be seen as an adaptive trait that can be under positive selection when changes in the environment outpace the rate of genetic evolutionary change. In cases when social plasticity is too costly or incomplete, behavioural consistency can emerge by directional selection that recruits gene modules corresponding to favoured behavioural states in that environment. As a result of this integrative approach, how knowledge of the proximate mechanisms underlying social plasticity is crucial to understanding its costs, limits and evolutionary consequences is shown, thereby highlighting the fact that proximate mechanisms contribute to the dynamics of selection. The role of teleosts as a premier model to study social plasticity is also highlighted, given the diversity and plasticity that this group exhibits in terms of social behaviour. Finally, the proposed integrative framework to social plasticity also illustrates how reciprocal causation analysis of biological phenomena (i.e. considering the interaction between proximate factors and evolutionary explanations) can be a more useful approach than the traditional proximate–ultimate dichotomy, according to which evolutionary processes can be understood without knowledge on proximate causes, thereby black‐boxing developmental and physiological mechanisms.     

Toward a stoichiometric framework for evolutionary biology

Ecological stoichiometry, the study of the balance of energy and materials in living systems, may serve as a useful synthetic framework for evolutionary biology. Here, we review recent work that illustrates the power of a stoichiometric approach to evolution across multiple scales, and then point to important open questions that may chart the way forward in this new field. At the molecular level, stoichiometry links hereditary changes in the molecular composition of organisms to key phenotypic functions. At the level of evolutionary ecology, a simultaneous focus on the energetic and material underpinnings of evolutionary tradeoffs and transactions highlights the relationship between the cost of resource acquisition and the functional consequences of biochemical composition. At the macroevolutionary level, a stoichiometric perspective can better operationalize models of adaptive radiation and escalation, and elucidate links between evolutionary innovation and the development of global biogeochemical cycles. Because ecological stoichiometry focuses on the interaction of energetic and multiple material currencies, it should provide new opportunities for coupling evolutionary dynamics across scales from genomes to the biosphere.     

Eco‐evolutionary dynamics in a coevolving host–virus system

Eco‐evolutionary dynamics have been shown to be important for understanding population and community stability and their adaptive potential. However, coevolution in the framework of eco‐evolutionary theory has not been addressed directly. Combining experiments with an algal host and its viral parasite, and mathematical model analyses we show eco‐evolutionary dynamics in antagonistic coevolving populations. The interaction between antagonists initially resulted in arms race dynamics (ARD) with selective sweeps, causing oscillating host–virus population dynamics. However, ARD ended and populations stabilised after the evolution of a general resistant host, whereas a trade‐off between host resistance and growth then maintained host diversity over time (trade‐off driven dynamics). Most importantly, our study shows that the interaction between ecology and evolution had important consequences for the predictability of the mode and tempo of adaptive change and for the stability and adaptive potential of populations.     

Extending eco-evolutionary theory with oligomorphic dynamics

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. ‘Oligomorphic dynamics’ can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.     

Undermatching is an emergent property of selection by consequences

A computational theory of selection by consequences [McDowell, J.J, 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297-317] was tested by studying the responding of virtual organisms that were animated by the theory on random interval schedules of reinforcement. The theory generated responding by applying principles of selection, reproduction, and mutation to a population of potential behaviors that evolved in response to the selection pressure exerted by reinforcement. The organisms' equilibrium response rates were well described by the modern version of the Herrnstein hyperbola, which includes an exponent on reinforcement rate. Under strong selection pressure this exponent decreased with increasing mutation rate from a value near 1.0 at 1% mutation to an asymptotic value of 0.83 at mutation rates of 10% and greater. This asymptotic value is consistent with values obtained by fitting the equation to data from live organisms responding on single schedules, and with the value of about 0.80 that is expected on the basis of extensive research with live organisms responding on concurrent schedules. These results show that the computational theory is consistent with the modern theory of matching [McDowell, J.J, 2005. On the classic and modern theories of matching. J. Exp. Anal. Behav. 84, 111-127], and that it is a viable candidate for a mathematical dynamics of behavior.     

GROUP FORMATION AND THE EVOLUTION OF SOCIALITY

In spite of its intrinsic evolutionary instability, altruistic behavior in social groups is widespread in nature, spanning from organisms endowed with complex cognitive abilities to microbial populations. In this study, we show that if social individuals have an enhanced tendency to form groups and fitness increases with group cohesion, sociality can evolve and be maintained in the absence of actively assortative mechanisms such as kin recognition or nepotism toward other carriers of the social gene. When explicitly taken into account in a game‐theoretical framework, the process of group formation qualitatively changes the evolutionary dynamics with respect to games played in groups of constant size and equal grouping tendencies. The evolutionary consequences of the rules underpinning the group size distribution are discussed for a simple model of microbial aggregation by differential attachment, indicating a way to the evolution of sociality bereft of peer recognition.     

Towards an artificial brain

Three components of a brain model operating on neuromolecular computing principles are described. The first component comprises neurons whose input-output behavior is controlled by significant internal dynamics. Models of discrete enzymatic neurons, reaction-diffusion neurons operating on the basis of the cyclic nucleotide cascade, and neurons controlled by cytoskeletal dynamics are described. The second component of the model is an evolutionary learning algorithm which is used to mold the behavior of enzyme-driven neurons or small networks of these neurons for specific function, usually pattern recognition or target seeking tasks. The evolutionary learning algorithm may be interpreted either as representing the mechanism of variation and natural selection acting on a phylogenetic time scale, or as a conceivable ontogenetic adaptation mechanism. The third component of the model is a memory manipulation scheme, called the reference neuron scheme. In principle it is capable of orchestrating a repertoire of enzyme-driven neurons for coherent function. The existing implementations, however, utilize simple neurons without internal dynamics. Spatial navigation and simple game playing (using tic-tac-toe) provide the task environments that have been used to study the properties of the reference neuron model. A memory-based evolutionary learning algorithm has been developed that can assign credit to the individual neurons in a network. It has been run on standard benchmark tasks, and appears to be quite effective both for conventional neural nets and for networks of discrete enzymatic neurons. The models have the character of artificial worlds in that they map the hierarchy of processes in the brain (at the molecular, neuronal, and network levels), provide a task environment, and use this relatively self-contained setup to develop and evaluate learning and adaptation algorithms.     

Genetic analysis of life-history constraint and evolution in a wild ungulate population

Trade-offs among life-history traits are central to evolutionary theory. In quantitative genetic terms, trade-offs may be manifested as negative genetic covariances relative to the direction of selection on phenotypic traits. Although the expression and selection of ecologically important phenotypic variation are fundamentally multivariate phenomena, the in situ quantification of genetic covariances is challenging. Even for life-history traits, where well-developed theory exists with which to relate phenotypic variation to fitness variation, little evidence exists from in situ studies that negative genetic covariances are an important aspect of the genetic architecture of life-history traits. In fact, the majority of reported estimates of genetic covariances among life-history traits are positive. Here we apply theory of the genetics and selection of life histories in organisms with complex life cycles to provide a framework for quantifying the contribution of multivariate genetically based relationships among traits to evolutionary constraint. We use a Bayesian framework to link pedigree-based inference of the genetic basis of variation in life-history traits to evolutionary demography theory regarding how life histories are selected. Our results suggest that genetic covariances may be acting to constrain the evolution of female life-history traits in a wild population of red deer Cervus elaphus: genetic covariances are estimated to reduce the rate of adaptation by about 40%, relative to predicted evolutionary change in the absence of genetic covariances. Furthermore, multivariate phenotypic (rather than genetic) relationships among female life-history traits do not reveal this constraint.     

Maternal effects, paternal effects and sexual selection

Maternal and paternal effects can lead to complicated evolutionary dynamics, including evolution in the opposite direction to selection. Recent studies demonstrate that parental effects on sexually selected traits, as well as preferences for those traits, might be large. Although these findings are likely to have consequences for both the evolutionary dynamics and equilibria of sexual selection, theory is lacking. Because parents are expected to maximize their own fitness, rather than that of a specific offspring, the magnitude (and even direction) of parental effects are context dependent. By extension, this dynamic nature of parental effects might help to explain the maintenance of variation in many sexually selected traits.     

Sociobiology and the structural stability of behavior patterns

A structural stability approach to population-genetic systems and to dynamic evolutionary games is attempted in order to examine the theoretical significance of sociobiological selection models. A criterion of weak selection is derived that is not restricted to differential reproduction in polymorphic systems but describes possible directions of evolutionary change in time scales governed by genetic mutation rates. The criterion applies to the problems of how the initial mutational basis of an adaptive trait may be established and how this may happen, for analogous traits, independently in different species. Two basic sociobiological concepts are reconsidered with reference to the criterion. It is shown that W. D. Hamilton's condition of increases in inclusive fitness due to altruistic interactions among kin expresses the structural instability of populations against the evolution of altruistic behavior. Using the dynamic approach to evolutionary game theory, it is demonstrated that if a behavioral phenotype is an evolutionarily stable strategy, it is structurally stable against perturbations of the fitness payoffs, provided selection is weak. These results are applied to material problems of the evolution of animal social behavior.     

Evolutionary game theoretic strategy for optimal drug delivery to influence selection pressure in treatment of HIV-1

Cytotoxic T-lymphocyte (CTL) escape mutation is associated with long-term behaviors of human immunodeficiency virus type 1 (HIV-1). Recent studies indicate heterogeneous behaviors of reversible and conservative mutants while the selection pressure changes. The purpose of this study is to optimize the selection pressure to minimize the long-term virus load. The results can be used to assist in delivery of highly loaded cognate peptide-pulsed dendritic cells (DC) into lymph nodes that could change the selection pressure. This mechanism may be employed for controlled drug delivery. A mathematical model is proposed in this paper to describe the evolutionary dynamics involving viruses and T cells. We formulate the optimization problem into the framework of evolutionary game theory, and solve for the optimal control of the selection pressure as a neighborhood invader strategy. The strategy dynamics can be obtained to evolve the immune system to the best controlled state. The study may shed light on optimal design of HIV-1 therapy based on optimization of adaptive CTL immune response.     

Symmetric competition as a general model for single-species adaptive dynamics

Adaptive dynamics is a widely used framework for modeling long-term evolution of continuous phenotypes. It is based on invasion fitness functions, which determine selection gradients and the canonical equation of adaptive dynamics. Even though the derivation of the adaptive dynamics from a given invasion fitness function is general and model-independent, the derivation of the invasion fitness function itself requires specification of an underlying ecological model. Therefore, evolutionary insights gained from adaptive dynamics models are generally model-dependent. Logistic models for symmetric, frequency-dependent competition are widely used in this context. Such models have the property that the selection gradients derived from them are gradients of scalar functions, which reflects a certain gradient property of the corresponding invasion fitness function. We show that any adaptive dynamics model that is based on an invasion fitness functions with this gradient property can be transformed into a generalized symmetric competition model. This provides a precise delineation of the generality of results derived from competition models. Roughly speaking, to understand the adaptive dynamics of the class of models satisfying a certain gradient condition, one only needs a complete understanding of the adaptive dynamics of symmetric, frequency-dependent competition. We show how this result can be applied to number of basic issues in evolutionary theory.     

Morphological interpretation of darwinism

The development of evolutionary theory requires the resolution of the problem of relationships between random and regular processes in historical development of biological systems. According to the theory of natural selection, ecological factors play a leading role in evolution. Variations are nondirectional, unpredictable, and provide chaotic diversity of variants, only some of which are potentially useful. However, based on random processes, new variants that are useful for organisms and remain adaptive significance in various ecological situations are infrequent. At the same time, morphology demonstrates certain evolutionary patterns. The morphological approach takes into account the role in evolution of structural features of organism and social systems and evolutionary significance of “constructive technologies,” which distinguish morphological interpretation of evolutionary processes. The constructive and evolutionary patterns revealed in biological systems provide the basis for morphological interpretation of the principle of natural selection: both natural and artificial selection is interaction between social systems (populations, ecosystems, biogeocoenoses) and organisms composing them.