Evolutionary dynamics of collective action in spatially structured populations.

Many models proposed to study the evolution of collective action rely on a formalism that represents social interactions as n-player games between individuals adopting discrete actions such as cooperate and defect. Despite the importance of spatial structure in biological collective action, the analysis of n-player games games in spatially structured populations has so far proved elusive. We address this problem by considering mixed strategies and by integrating discrete-action n-player games into the direct fitness approach of social evolution theory. This allows to conveniently identify convergence stable strategies and to capture the effect of population structure by a single structure coefficient, namely, the pairwise (scaled) relatedness among interacting individuals. As an application, we use our mathematical framework to investigate collective action problems associated with the provision of three different kinds of collective goods, paradigmatic of a vast array of helping traits in nature: "public goods" (both providers and shirkers can use the good, e.g., alarm calls), "club goods" (only providers can use the good, e.g., participation in collective hunting), and "charity goods" (only shirkers can use the good, e.g., altruistic sacrifice). We show that relatedness promotes the evolution of collective action in different ways depending on the kind of collective good and its economies of scale. Our findings highlight the importance of explicitly accounting for relatedness, the kind of collective good, and the economies of scale in theoretical and empirical studies of the evolution of collective action.

The Genomic Signature of Population Reconnection Following Isolation: From Theory to HIV.

Ease of worldwide travel provides increased opportunities for organisms not only to colonize new environments but also to encounter related but diverged populations. Such events of reconnection and secondary contact of previously isolated populations are widely observed at different time scales. For example, during the quaternary glaciation, sea water level fluctuations caused temporal isolation of populations, often to be followed by secondary contact. At shorter time scales, population isolation and reconnection of viruses are commonly observed, and such events are often associated with epidemics and pandemics. Here, using coalescent theory and simulations, we describe the temporal impact of population reconnection after isolation on nucleotide differences and the site frequency spectrum, as well as common summary statistics of DNA variation. We identify robust genomic signatures of population reconnection after isolation. We utilize our development to infer the recent evolutionary history of human immunodeficiency virus 1 (HIV-1) in Asia and South America, successfully retrieving the successive HIV subtype colonization events in these regions. Our analysis reveals that divergent HIV-1 subtype populations are currently admixing in these regions, suggesting that HIV-1 may be undergoing a process of homogenization, contrary to popular belief.

Evolutionary Ecology of Mountain Birch in Subarctic Stress Gradients: Interplay of Biotic and Abiotic Factors in Plant-Plant Interactions and Evolutionary Processes

In nature, variation for example in herbivory, wind exposure, moisture and pollution impact often creates variation in physiological stress and plant productivity. This variation is seldom clear-cut, but rather results in clines of decreasing growth and productivity towards the high-stress end. These clines of unidirectionally changing stress are generally known as ‘stress gradients’. Through its effect on plant performance, stress has the capacity to fundamentally alter the ecological relationships between individuals, and through variation in survival and reproduction it also causes evolutionary change, i.e. local adaptations to stress and eventually speciation. In certain conditions local adaptations to environmental stress have been documented in a matter of just a few generations. In plant-plant interactions, intensities of both negative interactions (competition) and positive ones (facilitation) are expected to vary along stress gradients. The stress-gradient hypothesis (SGH) suggests that net facilitation will be strongest in conditions of high biotic and abiotic stress, while a more recent ‘humpback’ model predicts strongest net facilitation at intermediate levels of stress. Plant interactions on stress gradients, however, are affected by a multitude of confounding factors, making studies of facilitation-related theories challenging. Among these factors are plant ontogeny, spatial scale, and local adaptation to stress. The last of these has very rarely been included in facilitation studies, despite the potential co-occurrence of local adaptations and changes in net facilitation in stress gradients. Current theory would predict both competitive effects and facilitative responses to be weakest in populations locally adapted to withstand high abiotic stress. This thesis is based on six experiments, conducted both in greenhouses and in the field in Russia, Norway and Finland, with mountain birch (Betula pubescens subsp. czerepanovii) as the model species. The aims were to study potential local adaptations in multiple stress gradients (both natural and anthropogenic), changes in plant-plant interactions under conditions of varying stress (as predicted by SGH), potential mechanisms behind intraspecific facilitation, and factors confounding plant-plant facilitation, such as spatiotemporal, ontogenetic, and genetic differences. I found rapid evolutionary adaptations (occurring within a time-span of 60 to 70 years) towards heavy-metal resistance around two copper-nickel smelters, a phenomenon that has resulted in a trade-off of decreased performance in pristine conditions. Heavy-metal-adapted individuals had lowered nickel uptake, indicating a possible mechanism behind the detected resistance. Seedlings adapted to heavy-metal toxicity were not co-resistant to others forms of abiotic stress, but showed co-resistance to biotic stress by being consumed to a lesser extent by insect herbivores. Conversely, populations from conditions of high natural stress (wind, drought etc.) showed no local adaptations, despite much longer evolutionary time scales. Due to decreasing emissions, I was unable to test SGH in the pollution gradients. In natural stress gradients, however, plant performance was in accordance with SGH, with the strongest host-seedling facilitation found at the high-stress sites in two different stress gradients. Factors confounding this pattern included (1) plant size / ontogenetic status, with seedling-seedling interactions being competition dominated and host-seedling interactions potentially switching towards competition with seedling growth, and (2) spatial distance, with competition dominating at very short planting distances, and facilitation being strongest at a distance of circa ¼ benefactor height. I found no evidence for changes in facilitation with respect to the evolutionary histories of plant populations. Despite the support for SGH, it may be that the ‘humpback’ model is more relevant when the main stressor is resource-related, while what I studied were the effects of ‘non-resource’ stressors (i.e. heavy-metal pollution and wind). The results have potential practical applications: the utilisation of locally adapted seedlings and plant facilitation may increase the success of future restoration efforts in industrial barrens as well as in other wind-exposed sites. The findings also have implications with regard to the effects of global change in subarctic environments: the documented potential by mountain birch for rapid evolutionary change, together with the general lack of evolutionary ‘dead ends’, due to not (over)specialising to current natural conditions, increase the chances of this crucial forest-forming tree persisting even under the anticipated climate change.

Interpersonal understanding and theory of mind

En populär idé inom dagens filosofiska och psykologiska forskning om interpersonlig förståelse, är idén att vi använder en kognitiv funktion (eller metod) för att förstå andra människor, en så kallad ”theory of mind” funktion. Denna idé förekommer inom ett brett vetenskapligt fält så som inom evolutionspsykologi, inom teorier om barns utveckling, inom teorier om autism, samt inom emotionsfilosofi och moralfilosofi. Avsikten i denna studie är att se närmare på vissa inflytelserika filosofiska och psykologiska teorier om interpersonlig förståelse, teorier som också har en stark koppling till empirisk forskning. I arbetet hävdar Gustafsson att teorierna ifråga avspeglar vissa klassiska, filosofiskt problematiska, antaganden. Dessa antaganden präglar teorierna ifråga samt påverkar hur de empiriska undersökningarna byggs upp och hur resultat tolkas.

The Power and Promise of Developmental Systems Theory

I argue that it is time for many feminists to rethink their attitudes towards evolutionary biology, not because feminists have been wrong to be deeply sceptical about many of its claims, both explicit and implicit, but because biology itself has changed. A new appreciation for the importance of development in biology has become mainstream and a new ontology, associated with developmental systems theory (DST), has been introduced over the last two decades. This turn challenges some of the features of evolutionary biology that have most troubled feminists. DST undermines the idea of biologicales sence and challenges both nature /nurture and nature/culture distinctions. Freed from these conceptual constraints, evolutionary biology no longer poses the problems that have justified feminist scepticism. Indeed, feminists have already found useful applications for DST and I argue that they should expand their use of DST to support more radical and wide-ranging political theories.

Population subdivision and molecular sequence variation: Theory and analysis of Drosophila ananassae data

Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to Mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter Theta to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of Theta, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

Parameter estimation based on stacked regression and evolutionary algorithms

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors are used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

Forecasting extinction risk of ectotherms under climate warming: an evolutionary perspective

1. It has been postulated that climate warming may pose the greatest threat species in the tropics, where ectotherms have evolved more thermal specialist physiologies. Although species could rapidly respond to environmental change through adaptation, little is known about the potential for thermal adaptation, especially in tropical species. 2. In the light of the limited empirical evidence available and predictions from mutation-selection theory, we might expect tropical ectotherms to have limited genetic variance to enable adaptation. However, as a consequence of thermodynamic constraints, we might expect this disadvantage to be at least partially offset by a fitness advantage, that is, the ‘hotter-is-better’ hypothesis. 3. Using an established quantitative genetics model and metabolic scaling relationships, we integrate the consequences of the opposing forces of thermal specialization and thermodynamic constraints on adaptive potential by evaluating extinction risk under climate warming. We conclude that the potential advantage of a higher maximal development rate can in theory more than offset the potential disadvantage of lower genetic variance associated with a thermal specialist strategy. 4. Quantitative estimates of extinction risk are fundamentally very sensitive to estimates of generation time and genetic variance. However, our qualitative conclusion that the relative risk of extinction is likely to be lower for tropical species than for temperate species is robust to assumptions regarding the effects of effective population size, mutation rate and birth rate per capita. 5. With a view to improving ecological forecasts, we use this modelling framework to review the sensitivity of our predictions to the model’s underpinning theoretical assumptions and the empirical basis of macroecological patterns that suggest thermal specialization and fitness increase towards the tropics. We conclude by suggesting priority areas for further empirical research.

An evolutionary innovation perspective on the selection of low and zero carbon technologies in new housing

The Code for Sustainable Homes (the Code) will require new homes in the United Kingdom to be ‘zero carbon’ from 2016. Drawing upon an evolutionary innovation perspective, this paper contributes to a gap in the literature by investigating which low and zero carbon technologies are actually being used by house builders, rather than the prevailing emphasis on the potentiality of these technologies. Using the results from a questionnaire three empirical contributions are made. First, house builders are selecting a narrow range of technologies. Second, these choices are made to minimise the disruption to their standard design and production templates (SDPTs). Finally, the coalescence around a small group of technologies is expected to intensify with solar-based technologies predicted to become more important. This paper challenges the dominant technical rationality in the literature that technical efficiency and cost benefits are the primary drivers for technology selection. These drivers play an important role but one which is mediated by the logic of maintaining the SDPTs of the house builders. This emphasises the need for construction diffusion of innovation theory to be problematized and developed within the context of business and market regimes constrained and reproduced by resilient technological trajectories.

On the evolutionary origins of obesity: a new hypothesis

Obesity is an escalating threat of pandemic proportions, currently affecting billions of people worldwide and exerting a devastating socioeconomic influence in industrialized countries. Despite intensive efforts to curtail obesity, results have proved disappointing. Although it is well recognized that obesity is a result of gene-environment interactions and that predisposition to obesity lies predominantly in our evolutionary past, there is much debate as to the precise nature of how our evolutionary past contributed to obesity. The “thrifty genotype” hypothesis suggests that obesity in industrialized countries is a throwback to our ancestors having undergone positive selection for genes that favored energy storage as a consequence of the cyclical episodes of famine and surplus after the advent of farming 10 000 years ago. Conversely, the “drifty genotype” hypothesis contends that the prevalence of thrifty genes is not a result of positive selection for energy-storage genes but attributable to genetic drift resulting from the removal of predative selection pressures. Both theories, however, assume that selection pressures the ancestors of modern humans living in western societies faced were the same. Moreover, neither theory adequately explains the impact of globalization and changing population demographics on the genetic basis for obesity in developed countries, despite clear evidence for ethnic variation in obesity susceptibility and related metabolic disorders. In this article, we propose that the modern obesity pandemic in industrialized countries is a result of the differential exposure of the ancestors of modern humans to environmental factors that began when modern humans left Africa around 70 000 years ago and migrated through the globe, reaching the Americas around 20 000 years ago. This article serves to elucidate how an understanding of ethnic differences in genetic susceptibility to obesity and the metabolic syndrome, in the context of historic human population redistribution, could be used in the treatment of obesity in industrialized countries

Fundamental insights into ontogenetic growth from theory and fish

The fundamental features of growth may be universal, because growth trajectories of most animals are very similar, but a unified mechanistic theory of growth remains elusive. Still needed is a synthetic explanation for how and why growth rates vary as body size changes, both within individuals over their ontogeny and between populations and species over their evolution. Here we use Bertalanffy growth equations to characterize growth of ray-finned fishes in terms of two parameters, the growth rate coefficient, K, and final body mass, m∞. We derive two alternative empirically testable hypotheses and test them by analyzing data from FishBase. Across 576 species, which vary in size at maturity by almost nine orders of magnitude, K scaled as m_∞^(-0.23). This supports our first hypothesis that growth rate scales as m_∞^(-0.25) as predicted by metabolic scaling theory; it implies that species which grow to larger mature sizes grow faster as juveniles. Within fish species, however, K scaled as m_∞^(-0.35). This supports our second hypothesis which predicts that growth rate scales as m_∞^(-0.33) when all juveniles grow at the same rate. The unexpected disparity between across- and within-species scaling challenges existing theoretical interpretations. We suggest that the similar ontogenetic programs of closely related populations constrain growth to m_∞^(-0.33) scaling, but as species diverge over evolutionary time they evolve the near-optimal m_∞^(-0.25) scaling predicted by metabolic scaling theory. Our findings have important practical implications because fish supply essential protein in human diets, and sustainable yields from wild harvests and aquaculture depend on growth rates.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.

Bootstrap confidence regions for canonical variates: Application to studies of evolutionary differentiation

Theory recently developed to construct confidence regions based on the parametric bootstrap is applied to add inferential information to graphical displays of sample centroids in canonical variate analysis. Problems of morphometric differentiation among subspecies and species are addressed using numerical resampling procedures.