Adaptive introgression and de novo mutations increase access to novel fitness peaks on the fitness landscape during a vertebrate adaptive radiation

Estimating the complex relationship between fitness and genotype or phenotype (i.e. the adaptive landscape) is one of the central goals of evolutionary biology. Empirical fitness landscapes have now been estimated for numerous systems, from phage to proteins to finches. However, the nature of adaptive walks connecting genotypes to organismal fitness, speciation, and novel ecological niches are still poorly understood. One outstanding system for addressing these connections is a recent adaptive radiation of ecologically and morphologically distinct pupfishes (a generalist, molluscivore, and scale-eater) endemic to San Salvador Island, Bahamas. Here, we leveraged whole-genome sequencing of 139 hybrids from two independent field fitness experiments to identify the genomic basis of fitness, visualize the first genotypic fitness networks in a vertebrate system, and infer the contributions of different sources of genetic variation to the accessibility of the fitness landscape. We identified 132 SNPs that were significantly associated with fitness in field enclosures, including six associated genes that were differentially expressed between specialists, and one gene (protein-lysine methyltransferase: METTL21E) misexpressed in hybrids, suggesting a potential intrinsic genetic incompatibility. We then constructed genotypic fitness networks from adaptive alleles and show that only introgressed and de novo variants, not standing genetic variation, increased the accessibility of genotypic fitness paths from generalist to specialists. Our results suggest that adaptive introgression and de novo variants provided key connections in adaptive walks necessary for crossing fitness valleys and triggering the evolution of novelty during adaptive radiation.

The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

Climbing Mount Probable

Chapter 8 provides the formal basis to recognize biases in the introduction of variation as a cause of evolutionary biases. The shifting-gene-frequencies theory of the Modern Synthesis posits a “buffet” view in which evolution is merely a process of shifting the frequencies of pre-existing alleles, without new mutations. Within this theory, mutation is represented like selection or drift, as a “force” that shifts frequencies. Yet, within a broader conception of evolution, a second kind of causal process is required: an introduction process that can shift a frequency upwards from 0, which selection and drift cannot do. Abstract models demonstrate the influence of biases in the introduction process in one-step and multi-step adaptive walks. Such biases do not require mutation biases per se, but may arise from effects of development, and from the differential accessibility of alternative forms in abstract possibility-spaces.

The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling the interactive effects of de novo biological change and multivariate trait correlation evolution using principal component axes. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that only a limited number of phenotypes emerged. Applying adaptive trait correlations to the starting population (historical bias) accelerated adaptation while highly convergent, nonrandom phenotypic solutions emerged irrespective of bias. These findings are consistent with a limited set of evolutionary trajectories underlying the vast amount of possible trait combinations (phenotypes). Critically, we demonstrate that these dynamics emerge in an empirically defined multidimensional trait space and show that trait correlations, in addition to trait values, must evolve to explain multi-trait adaptation. Identifying high probability high-fitness outcomes based on trait correlations is necessary in order to connect microbial evolutionary responses to biogeochemical cycling, thereby enabling the incorporation of these dynamics into global ecosystem models.

Adaptive Walks and Fitness Peak Climbing: Language Learning as Inducing Adaptive Change in L2 Systems

This paper adopts the perspective of students, groups, and entire classes as being complex adaptive systems, or CAS. Two important concepts from complexity theory, adaptive walks and fitness landscapes, can be used to create optimal language learning conditions for producing changes in students’ L2 systems. Every configuration of L2 traits a student might have can be assigned a fitness value, that is, an L2 competence level. The set of all such values creates an abstract landscape, the fitness landscape. By changing traits, the position on the landscape changes, meaning a student can take a journey on the landscape, known as an adaptive walk, in the search for higher peaks, that is, higher competence. The goal becomes establishing conditions in which adaptive walks are encouraged. Several L2 activities are introduced as applications of these ideas. 学生、グループ、およびクラス全体は、複雑系適応システムと考えられる。複雑系理論における2つの概念、アダプティブ・ウォーク(適応型歩行)とフィットネス・ランドスケープは、言語学習に最適な条件を創り出す一助となる。それぞれの学生は皆、文法や発音など異なるレベルのL2 スキルを保有している。これらのスキルを組み合わせることで全体的な L2 能力すなわちフィットネス・バリューを生み出すことが可能となる。このような異なるレベルの組み合わせによる全てのユニットとそれに伴うフィットネス・バリューはフィットネス・ランドスケーブ(適応度地形)を創りだす。ある L2 スキルが向上または低下する時、フィットネス・バリューは変化する。それによってランドスケープ上の位置が変化する。スキルのレベル変化を通じて学生はアダプティブ・ウォークとして知られるランドスケープを旅する。旅の到達目標は、アダプティブ・ウォークを奨励してランドスケープ上の高位のフィットネルピークを見出すことである。これらの理念の応用としていくつかの L2 アクティビティを導入する。

Analysis of statistical correlations between properties of adaptive walks in fitness landscapes

The fitness landscape metaphor has been central in our way of thinking about adaptation. In this scenario, adaptive walks are idealized dynamics that mimic the uphill movement of an evolving population towards a fitness peak of the landscape. Recent works in experimental evolution have demonstrated that the constraints imposed by epistasis are responsible for reducing the number of accessible mutational pathways towards fitness peaks. Here, we exhaustively analyse the statistical properties of adaptive walks for two empirical fitness landscapes and theoretical NK landscapes. Some general conclusions can be drawn from our simulation study. Regardless of the dynamics, we observe that the shortest paths are more regularly used. Although the accessibility of a given fitness peak is reasonably correlated to the number of monotonic pathways towards it, the two quantities are not exactly proportional. A negative correlation between predictability and mean path divergence is established, and so the decrease of the number of effective mutational pathways ensures the convergence of the attraction basin of fitness peaks. On the other hand, other features are not conserved among fitness landscapes, such as the relationship between accessibility and predictability.

Remodeling Ancestral Phenotypic Plasticity in Local Adaptation: A New Framework to Explore the Role of Genetic Compensation in the Evolution of Homeostasis

Phenotypic plasticity is not universally adaptive. In certain cases, plasticity can result in phenotypic shifts that reduce fitness relative to the un-induced state. A common cause of such maladaptive plasticity is the co-option of ancestral developmental and physiological response systems to meet novel challenges. Because these systems evolved to meet specific challenges in an ancestral environment (e.g., localized and transient hypoxia), their co-option to meet a similar, but novel, stressor (e.g., reductions in ambient pO2 at high elevation) can lead to misdirected responses that reduce fitness. In such cases, natural selection should act to remodel phenotypic plasticity to suppress the expression of these maladaptive responses. Because these maladaptive responses reduce the fitness of colonizers in new environments, this remodeling of ancestral plasticity may be among the earliest steps in adaptive walks toward new local optima. Genetic compensation has been proposed as a general form of adaptive evolution that leads to the suppression of maladaptive plasticity to restore the ancestral trait value in the face of novel stimuli. Given their central role in the regulation of basic physiological functions, we argue that genetic compensation may often be achieved by modifications of homeostatic regulatory systems. We further suggest that genetic compensation to modify homeostatic systems can be achieved by two alternative strategies that differ in their mechanistic underpinnings; to our knowledge, these strategies have not been formally recognized by previous workers. We then consider how the mechanistic details of these alternative strategies may constrain their evolution. These considerations lead us to argue that genetic compensation is most likely to evolve by compensatory physiological changes that safeguard internal homeostatic conditions to prevent the expression of maladaptive portions of conserved reaction norms, rather than direct evolution of plasticity itself. Finally, we outline a simple experimental framework to test this hypothesis. Our goal is to stimulate research aimed at providing a deeper mechanistic understanding of whether and how phenotypic plasticity can be remodeled following environmental shifts that render ancestral responses maladaptive, an issue with increasing importance in our current era of rapid environmental change.

Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics

The dynamics of evolution is intimately shaped by epistasis — interactions between genetic elements which cause the fitness-effect of combinations of mutations to be non-additive. Analyzing evolutionary dynamics that involves large numbers of epistatic mutations is intrinsically difficult. A crucial feature is that the fitness landscape in the vicinity of the current genome depends on the evolutionary history. A key step is thus developing models that enable study of the effects of past evolution on future evolution. In this work, we introduce a broad class of high-dimensional random fitness landscapes for which the correlations between fitnesses of genomes are a general function of genetic distance. Their Gaussian character allows for tractable computational as well as analytic understanding. We study the properties of these landscapes focusing on the simplest evolutionary process: random adaptive (uphill) walks. Conventional measures of “ruggedness” are shown to not much affect such adaptive walks. Instead, the long-distance statistics of epistasis cause all properties to be highly conditional on past evolution, determining the statistics of the local landscape (the distribution of fitness-effects of available mutations and combinations of these), as well as the global geometry of evolutionary trajectories. In order to further explore the effects of conditioning on past evolution, we model the effects of slowly changing environments. At long times, such fitness “seascapes” cause a statistical steady state with highly intermittent evolutionary dynamics: populations undergo bursts of rapid adaptation, interspersed with periods in which adaptive mutations are rare and the population waits for more new directions to be opened up by changes in the environment. Finally, we discuss prospects for studying more complex evolutionary dynamics and on broader classes of high-dimensional landscapes and seascapes.