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Mutation enables evolution, but the idea that adaptation is also shaped by mutational variation is controversial1–4. Simple evolutionary hypotheses predict such a relationship if the supply of mutations constrains evolution5,6, but it is not clear that constraints exist, and, even if they do, they may be overcome by long-term natural selection. Quantification of the relationship between mutation and phenotypic divergence among species will help to resolve these issues. Here we use precise data on over 50,000 Drosophilid fly wings to demonstrate unexpectedly strong positive relationships between variation produced by mutation, standing genetic variation, and the rate of evolution over the last 40 million years. Our results are inconsistent with simple constraint hypotheses because the rate of evolution is very low relative to what both mutational and standing variation could allow. In principle, the constraint hypothesis could be rescued if the vast majority of mutations are so deleterious that they cannot contribute to evolution, but this also requires the implausible assumption that deleterious mutations have the same pattern of effects as potentially advantageous ones. Our evidence for a strong relationship between mutation and divergence in a slowly evolving structure challenges the existing models of mutation in evolution.

Integration and modularity refer to the patterns and processes of trait interaction and independence. Both terms have complex histories with respect to both conceptualization and quantification, resulting in a plethora of integration indices in use. We review briefly the divergent definitions, uses and measures of integration and modularity and make conceptual links to allometry. We also discuss how integration and modularity might evolve. Although integration is generally thought to be generated and maintained by correlational selection, theoretical considerations suggest the relationship is not straightforward. We caution here against uncontrolled comparisons of indices across studies. In the absence of controls for trait number, dimensionality, homology, development and function, it is difficult, or even impossible, to compare integration indices across organisms or traits. We suggest that care be invested in relating measurement to underlying theory or hypotheses, and that summative, theory-free descriptors of integration generally be avoided. The papers that follow in this Theme Issue illustrate the diversity of approaches to studying integration and modularity, highlighting strengths and pitfalls that await researchers investigating integration in plants and animals.

Genetic architecture, the structure of the mapping from genotype to phenotype, determines the variational properties of the phenotype and is instrumental in understanding its evolutionary potential. Throughout most of the history of evolutionary biology, genetic architecture has been treated as a given set of parameters and not as a set of dynamic variables. The past decade has seen renewed interest in incorporating the genotype-phenotype map as a dynamical part of population genetics. This has been aided by several conceptual advances. I review these developments with emphasis on recent theoretical work on the evolution of genetic architecture and evolvability.

Microevolutionary studies often find that complex quantitative characters are highly evolvable and adapted to the local environment, while macroevolutionary studies often show evidence of strong phylogenetic effects and stasis. In this contribution, we show how phylogenetic comparative methods can be used to test hypotheses that may help resolve this paradox. As a test case, we studied the interplay between adaptation and phylogenetic inertia on the thermobiology of 32 species of Liolaemus (Squamata: Liolaemidae), a genus of South American lizards living under diverse climatic conditions. Despite a strong phylogenetic effect in the preferred (selected) body temperature, we found clear evidence that this variable is adapted to local temperature and climate. After controlling for adaptation to the thermal environment, little influence of phylogeny was left. This indicates that the phylogenetic effect was not caused by a lag or slowness in adaptation but primarily by the distribution of the thermal environments on the phylogeny. This can be due to thermal niche tracking. In contrast, we found little or no evidence for adaptation to the thermal environment in either cooling or heating rates, critical thermal minimum, or body size.

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.

A major challenge in evolutionary biology is understanding how stochastic and deterministic factors interact and influence macroevolutionary dynamics in natural populations. One classical approach is to record frequency changes of heritable and visible genetic polymorphisms over multiple generations. Here, we combined this approach with a maximum likelihood–based population-genetic model with the aim of understanding and quantifying the evolutionary processes operating on a female mating polymorphism in the blue-tailed damselfly Ischnura elegans. Previous studies on this color-polymorphic species have suggested that males form a search image for females, which leads to excessive mating harassment of common female morphs. We analyzed a large temporally and spatially replicated data set of between-generation morph frequency changes in I. elegans. Morph frequencies were more stable than expected from genetic drift alone, suggesting the presence of selection toward a stable equilibrium that prevents local loss or fixation of morphs. This can be interpreted as the signature of negative frequency-dependent selection maintaining the phenotypic stasis and genetic diversity in these populations. Our novel analytical approach allows the estimation of the strength of frequency-dependent selection from the morph frequency fluctuations around their inferred long-term equilibria. This approach can be extended and applied to other polymorphic organisms for which time-series data across multiple generations are available.

Morphological traits often covary within and among species according to simple power laws referred to as allometry. Such allometric relationships may result from common growth regulation, and this has given rise to the hypothesis that allometric exponents may have low evolvability and constrain trait evolution. We formalize hypotheses for how allometry may constrain morphological trait evolution across taxa, and test these using more than 300 empirical estimates of static (within-species) allometric relations of animal morphological traits. Although we find evidence for evolutionary changes in allometric parameters on million-year, cross-species time scales, there is limited evidence for microevolutionary changes in allometric slopes. Accordingly, we find that static allometries often predict evolutionary allometries on the subspecies level, but less so across species. Although there is a large body of work on allometry in a broad sense that includes all kinds of morphological trait–size relationships, we found relatively little information about the evolution of allometry in the narrow sense of a power relationship. Despite the many claims of microevolutionary changes of static allometries in the literature, hardly any of these apply to narrow-sense allometry, and we argue that the hypothesis of strongly constrained static allometric slopes remains viable.

Measurement—the assignment of numbers to attributes of the natural world—is central to all scientific inference. Measurement theory concerns the relationship between measurements and reality; its goal is ensuring that inferences about measurements reflect the underlying reality we intend to represent. The key principle of measurement theory is that theoretical context, the rationale for collecting measurements, is essential to defining appropriate measurements and interpreting their values. Theoretical context determines the scale type of measurements and which transformations of those measurements can be made without compromising their meaningfulness. Despite this central role, measurement theory is almost unknown in biology, and its principles are frequently violated. In this review, we present the basic ideas of measurement theory and show how it applies to theoretical as well as empirical work. We then consider examples of empirical and theoretical evolutionary studies whose meaningfulness have been compromised by violations of measurement-theoretic principles. Common errors include not paying attention to theoretical context, inappropriate transformations of data, and inadequate reporting of units, effect sizes, or estimation error. The frequency of such violations reveals the importance of raising awareness of measurement theory among biologists.

Many traits scale precisely with size, but it is unknown whether this is due to selection for optimal function or due to evolutionary constraint. We use artificial selection to demonstrate that wing-shape scaling in fruit flies can respond to selection. This evolved response in scaling was lost during a few generations after selection ended, but other selected changes in wing shape persisted. Shape–size scaling in fly wings is therefore evolvable, but adaptation is apparently constrained by selection that may not be on wings. This may explain why scaling relationships are often evolutionarily conserved. Precise exponential scaling with size is a fundamental aspect of phenotypic variation. These allometric power laws are often invariant across taxa and have long been hypothesized to reflect developmental constraints. Here we test this hypothesis by investigating the evolutionary potential of an allometric scaling relationship in drosophilid wing shape that is nearly invariant across 111 species separated by at least 50 million years of evolution. In only 26 generations of artificial selection in a population of Drosophila melanogaster, we were able to drive the allometric slope to the outer range of those found among the 111 sampled species. This response was rapidly lost when selection was suspended. Only a small proportion of this reversal could be explained by breakup of linkage disequilibrium, and direct selection on wing shape is also unlikely to explain the reversal, because the more divergent wing shapes produced by selection on the allometric intercept did not revert. We hypothesize that the reversal was instead caused by internal selection arising from pleiotropic links to unknown traits. Our results also suggest that the observed selection response in the allometric slope was due to a component expressed late in larval development and that variation in earlier development did not respond to selection. Together, these results are consistent with a role for pleiotropic constraints in explaining the remarkable evolutionary stability of allometric scaling.

Background While ants are textbook examples of eusocial animals in which altruistic behavior is maintained through kin selection, several ants form genetically diverse colonies that challenge this concept. One example is the Australian green-head ant ( Rhytidoponera metallica ) whose colonies harbor such extreme genetic variation that they have been speculated to represent an unstable form of eusociality. Yet, R. metallica is among the most successful ants on the Australian subcontinent. This success has been hypothesized to be partly due to the diverse venoms harbored within each colony. However, the genomic basis and evolutionary scenarios that maintain this toxin diversity remain unknown. Results To examine toxin genomic architecture, quantify individual-level genetic variation, and identify both proximate and ultimate mechanisms that have facilitated the toxin diversity in R. metallica , we generate a high-quality draft genome from a single worker. Most ectatotoxin genes are in clusters that contain evidence of multiple, complex gene-family expansions, some of which are likely explained by the presence of transposable elements. We also show that toxin regions of the genome exhibit elevated genetic variation despite being under strong selection and that this variation can translate to phenotypic diversity through toxin alleles with different functional properties. Conclusions Taken together, our results point to classical gene duplication and diversification as the main evolutionary mechanism by which the main toxin family in ant venoms evolves, suggest toxin-gene functional diversification under frequency-dependent selection maintains colony-level venom hypervariability in R. metallica , and provide new insight into the role of multi-level selection in eusocial animals.