Thomas F. Hansen

STABILIZING SELECTION AND THE COMPARATIVE ANALYSIS OF ADAPTATION

Comparative studies tend to differ from optimality and functionality studies in how they treat adaptation. While the comparative approach focuses on the origin and change of traits, optimality studies assume that adaptations are maintained at an optimum by stabilizing selection. This paper presents a model of adaptive evolution on a macroevolutionary time scale that includes the maintenance of traits at adaptive optima by stabilizing selection as the dominant evolutionary force. Interspecific variation is treated as variation in the position of adaptive optima. The model illustrates how phylogenetic constraints not only lead to correlations between phylogenetically related species, but also to imperfect adaptations. From this model, a statistical comparative method is derived that can be used to estimate the effect of a selective factor on adaptive optima in a way that would be consistent with an optimality study of adaptation to this factor. The method is illustrated with an analysis of dental evolution in fossil horses. The use of comparative methods to study evolutionary trends is also discussed.

PERSPECTIVE:EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS

Abstract Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness—genetic robustness—and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness; and (3) exploring the implications of genetic robustness for future evolution.

Measuring and comparing evolvability and constraint in multivariate characters

The Lande equation forms the basis for our understanding of the short‐term evolution of quantitative traits in a multivariate context. It predicts the response to selection as the product of an additive genetic variance matrix and a selection gradient. The selection gradient approximates the force and direction of selection, and the genetic variance matrix quantifies the role of the genetic system in evolution. Attempts to understand the evolutionary significance of the genetic variance matrix are hampered by the fact that the majority of the methods used to characterize and compare variance matrices have not been derived in an explicit theoretical context. We use the Lande equation to derive new measures of the ability of a variance matrix to allow or constrain evolution in any direction in phenotype space. Evolvability captures the ability of a population to evolve in the direction of selection when stabilizing selection is absent. Conditional evolvability captures the ability of a population to respond to directional selection in the presence of stabilizing selection on other trait combinations. We then derive measures of character autonomy and integration from these evolvabilities. We study the properties of these measures and show how they can be used to interpret and compare variance matrices. As an illustration, we show that divergence of wing shape in the dipteran family Drosophilidae has proceeded in directions that have relatively high evolvabilities.

Comparing strengths of directional selection: how strong is strong?

Abstract The fundamental equation in evolutionary quantitative genetics, the Lande equation, describes the response to directional selection as a product of the additive genetic variance and the selection gradient of trait value on relative fitness. Comparisons of both genetic variances and selection gradients across traits or populations require standardization, as both are scale dependent. The Lande equation can be standardized in two ways. Standardizing by the variance of the selected trait yields the response in units of standard deviation as the product of the heritability and the variance‐standardized selection gradient. This standardization conflates selection and variation because the phenotypic variance is a function of the genetic variance. Alternatively, one can standardize the Lande equation using the trait mean, yielding the proportional response to selection as the product of the squared coefficient of additive genetic variance and the mean‐standardized selection gradient. Mean‐standardized selection gradients are particularly useful for summarizing the strength of selection because the mean‐standardized gradient for fitness itself is one, a convenient benchmark for strong selection. We review published estimates of directional selection in natural populations using mean‐standardized selection gradients. Only 38 published studies provided all the necessary information for calculation of mean‐standardized gradients. The median absolute value of multivariate mean‐standardized gradients shows that selection is on average 54% as strong as selection on fitness. Correcting for the upward bias introduced by taking absolute values lowers the median to 31%, still very strong selection. Such large estimates clearly cannot be representative of selection on all traits. Some possible sources of overestimation of the strength of selection include confounding environmental and genotypic effects on fitness, the use of fitness components as proxies for fitness, and biases in publication or choice of traits to study.

A Comparative Method for Studying Adaptation to a Randomly Evolving Environment

Abstract Most phylogenetic comparative methods used for testing adaptive hypotheses make evolutionary assumptions that are not compatible with evolution toward an optimal state. As a consequence they do not correct for maladaptation. The “evolutionary regression” that is returned is more shallow than the optimal relationship between the trait and environment. We show how both evolutionary and optimal regressions, as well as phylogenetic inertia, can be estimated jointly by a comparative method built around an Ornstein–Uhlenbeck model of adaptive evolution. The method considers a single trait adapting to an optimum that is influenced by one or more continuous, randomly changing predictor variables.

Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability

Evolvability is the ability to respond to a selective challenge. This requires the capacity to produce the right kind of variation for selection to act upon. To understand evolvability we therefore need to understand the variational properties of biological organisms. Modularity is a variational property, which has been linked to evolvability. If different characters are able to vary independently, selection will be able to optimize each character separately without interference. But although modularity seems like a good design principle for an evolvable organism, it does not therefore follow that it is the only design that can achieve evolvability. In this essay I analyze the effects of modularity and, more generally, pleiotropy on evolvability. Although, pleiotropy causes interference between the adaptation of different characters, it also increases the variational potential of those characters. The most evolvable genetic architectures may often be those with an intermediate level of integration among characters, and in particular those where pleiotropic effects are variable and able to compensate for each others constraints.

Pelagic food web configurations at different levels of nutrient richness and their implications for the ratio fish production : primary production

Based on existing knowledge about phytoplankton responses to nutrients and food size spectra of herbivorous zooplankton, three different configurations of pelagic food webs are proposed for three different types of marine nutrient regimes: (1) upwelling systems, (2) oligotrophic oceanic systems, (3) eutrophicated coastal systems. Up-welling systems are characterised by high levels of plant nutrients and high ratios of Si to N and R. Phytoplankton consists mainly of diatoms together with a subdominant contribution of flagellates. Most phytoplankton falls into the food spectrum of herbivorous, crustacean zooplankton. Therefore, herbivorous crustaceans occupy trophic level 2 and zooplanktivorous fish occupy trophic level 3. Phytoplankton in oligotrophic, oceanic systems is dominated by picoplankton, which are too small to be ingested by copepods. Most primary production is channelled through the ‘microbial loop’ (picoplankton — heterotrophic nanoflagellates — ciliates). Sporadically, pelagic tunicates also consume a substantial proportion of primary production. Herbivorous crustaceans feed on heterotrophic nanoflagellates and ciliates, thus occupying a food chain position between 3 and 4, which leads to a food chain position between 4 and 5 for zooplanktivorous fish. By cultural eutrophication, N and P availability are elevated while Si remains unaffected or even declines. Diatoms decrease in relative importance while summer blooms of inedible algae (Phaeocystis, toxic dinoflagellates, toxic prymnesiophyceae, etc.) prevail. The spring bloom may still contain a substantial contribution of diatoms. The production of the inedible algae enters the pelagic energy flow via the detritus food chain: DOC release by cell lysis — bacteria — heterotrophic nanoflagellates — ciliates. Accordingly, crustacean zooplankton occupy food chain position 4 to 5 during the non-diatom seasons. Ecological efficiency considerations lead to the conclusion that fish production:primary production ratios should be highest in upwelling systems and substantially lower in oligotrophic and in culturally eutrophicated systems. Further losses of fish production may occur when carnivorous, gelatinous zooplankton (jellyfish) replace fish.

Good genes and old age: Do old mates provide superior genes?

It has been suggested that female preference for older mates in species without parental care has evolved in response to an alleged higher genetic quality of older individuals. This is based on the widespread assumption that viability selection produces older individuals that are genetically superior to younger individuals. In contrast, we propose that the oldest individuals rarely are genetically superior. Quantitative genetic models of life history evolution indicate that young to intermediately aged individuals are likely to possess the highest breeding values of fitness. This conclusion is based on four arguments: 1) Viability selection on older individuals may decrease or at least not substantially increase breeding values of fitness, because there may exist negative genetic correlations between late‐age and early‐age life history parameters, 2) Fertility selection is likely to raise the fitness of gametes produced by young individuals more than those produced by old individuals, because the covariance between fertility and fitness often decreases with age, 3) The history of selection on their parents makes younger individuals more fit than older individuals, 4) Germ‐line mutations, which are generally deleterious, significantly decrease the breeding value of fitness of an individual throughout its lifespan, especially in males. Therefore, females that mate with the oldest males in a population are doing so for reasons other than to obtain offspring of high genetic quality.

Heritability is not Evolvability

Short-term evolutionary potential depends on the additive genetic variance in the population. The additive variance is often measured as heritability, the fraction of the total phenotypic variance that is additive. Heritability is thus a common measure of evolutionary potential. An alternative is to measure evolutionary potential as expected proportional change under a unit strength of selection. This yields the mean-scaled additive variance as a measure of evolvability. Houle in Genetics 130:195–204, (1992) showed that these two ways of scaling additive variance are often inconsistent and can lead to different conclusions as to what traits are more evolvable. Here, we explore this relation in more detail through a literature review, and through theoretical arguments. We show that the correlation between heritability and evolvability is essentially zero, and we argue that this is likely due to inherent positive correlations between the additive variance and other components of phenotypic variance. This means that heritabilities are unsuitable as measures of evolutionary potential in natural populations. More generally we argue that scaling always involves non-trivial assumptions, and that a lack of awareness of these assumptions constitutes a systemic error in the field of evolutionary biology.

The million-year wait for macroevolutionary bursts

We lack a comprehensive understanding of evolutionary pattern and process because short-term and long-term data have rarely been combined into a single analytical framework. Here we test alternative models of phenotypic evolution using a dataset of unprecedented size and temporal span (over 8,000 data points). The data are body-size measurements taken from historical studies, the fossil record, and among-species comparative data representing mammals, squamates, and birds. By analyzing this large dataset, we identify stochastic models that can explain evolutionary patterns on both short and long timescales and reveal a remarkably consistent pattern in the timing of divergence across taxonomic groups. Even though rapid, short-term evolution often occurs in intervals shorter than 1 Myr, the changes are constrained and do not accumulate over time. Over longer intervals (1–360 Myr), this pattern of bounded evolution yields to a pattern of increasing divergence with time. The best-fitting model to explain this pattern is a model that combines rare but substantial bursts of phenotypic change with bounded fluctuations on shorter timescales. We suggest that these rare bursts reflect permanent changes in adaptive zones, whereas the short-term fluctuations represent local variations in niche optima due to restricted environmental variation within a stable adaptive zone.