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The genotype-phenotype map determines how genetic variation translates into traits, influencing evolutionary adaptability. While previous models often assume a static relationship, genetic architectures evolve dynamically in response to selective pressures. In this study, we investigate how epistasis and pleiotropy adapt under varying fitness landscape ruggedness (K) and environmental variability (V ) together, shaping genetic robustness and evolvability. Using the NK treadmill model, we systematically explore the independent effects of K and V on genetic complexity. Our findings reveal that increased ruggedness (K) reduces genetic interdependencies, favoring modular architectures that enhance mutational robustness. Conversely, higher environmental variability (V ) promotes interconnected genetic networks, increasing evolvability. Empirical validation using bacterial genomes supports these results, showing strong correlations between genetic complexity measures and mutational robustness, reinforcing the role of environmental pressures in shaping genetic architectures.

Why animal communication displays are so complex and how they have evolved are active foci of research with a long and rich history. Progress towards an evolutionary analysis of signal complexity, however, has been constrained by a lack of hypotheses to explain similarities and/or differences in signalling systems across taxa. To address this, we advocate incorporating a systems approach into studies of animal communication—an approach that includes comprehensive experimental designs and data collection in combination with the implementation of systems concepts and tools. A systems approach evaluates overall display architecture, including how components interact to alter function, and how function varies in different states of the system. We provide a brief overview of the current state of the field, including a focus on select studies that highlight the dynamic nature of animal signalling. We then introduce core concepts from systems biology (redundancy, degeneracy, pluripotentiality, and modularity) and discuss their relationships with system properties (e.g. robustness, flexibility, evolvability). We translate systems concepts into an animal communication framework and accentuate their utility through a case study. Finally, we demonstrate how consideration of the system-level organization of animal communication poses new practical research questions that will aid our understanding of how and why animal displays are so complex.

A recurrent theme in evolutionary biology is to contrast natural selection and developmental constraint – two forces pitted against each other as competing explanations for organismal form. Despite its popularity, this juxtaposition is deeply misleading... Phenotypic variation is generated by the processes of development, with some variants arising more readily than others—a phenomenon known as “developmental bias.” Developmental bias and natural selection have often been portrayed as alternative explanations, but this is a false dichotomy: developmental bias can evolve through natural selection, and bias and selection jointly influence phenotypic evolution. Here, we briefly review the evidence for developmental bias and illustrate how it is studied empirically. We describe recent theory on regulatory networks that explains why the influence of genetic and environmental perturbation on phenotypes is typically not uniform, and may even be biased toward adaptive phenotypic variation. We show how bias produced by developmental processes constitutes an evolving property able to impose direction on adaptive evolution and influence patterns of taxonomic and phenotypic diversity. Taking these considerations together, we argue that it is not sufficient to accommodate developmental bias into evolutionary theory merely as a constraint on evolutionary adaptation. The influence of natural selection in shaping developmental bias, and conversely, the influence of developmental bias in shaping subsequent opportunities for adaptation, requires mechanistic models of development to be expanded and incorporated into evolutionary theory. A regulatory network perspective on phenotypic evolution thus helps to integrate the generation of phenotypic variation with natural selection, leaving evolutionary biology better placed to explain how organisms adapt and diversify.

Despite sophisticated mathematical models, the theory of microevolution is mostly treated as a qualitative rather than a quantitative tool. Numerical measures of selection, constraints, and evolutionary potential are often too loosely connected to theory to provide operational predictions of the response to selection. In this paper, we study the ability of a set of operational measures of evolvability and constraint to predict short-term selection responses generated by individual-based simulations. We focus on the effects of selective constraints under which the response in one trait is impeded by stabilizing selection on other traits. The conditional evolvability is a measure of evolutionary potential explicitly developed for this situation. We show that the conditional evolvability successfully predicts rates of evolution in an equilibrium situation, and further that these equilibria are reached with characteristic times that are inversely proportional to the fitness load generated by the constraining characters. Overall, we find that evolvabilities and conditional evolvabilities bracket responses to selection, and that they together can be used to quantify evolutionary potential on time scales where the G-matrix remains relatively constant.

Plant secondary metabolites (PSMs) are ubiquitous in plants and playmany ecological roles. Each compound can vary in presence and/or quantity, and the composition of the mixture of chemicals can vary, such that chemodiversity can be partitioned within and among individuals. Plant ontogeny and environmental and genetic variation are recognized as sources of chemical variation, but recent advances in understanding the molecular basis of variation may allow the future deployment of isogenic mutants to test the specific adaptive function of variation in PSMs. An important consequence of high intraspecific variation is the capacity to evolve rapidly. It is becoming increasingly clear that trait variance linked to both macro- and micro-environmental variation can also evolve and mayrespond more strongly to selection than mean trait values. This research, which is in its infancy in plants, highlights what could be a missing piece of the picture of PSM evolution. PSM polymorphisms are probably maintained by multiple selective forces acting across many spatial and temporal scales, but convincing examples that recognize the diversity of plant population structures are rare. We describe how diversity can be inherently beneficial for plants and suggest fruitful avenues for future research to untangle the causes and consequences of intraspecific variation.

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.

As organisms evolve, the effects of mutations change as a result of epistatic interactions with other mutations accumulated along the line of descent. This can lead to shifts in adaptability or robustness that ultimately shape subsequent evolution. Here, we review recent advances in measuring, modeling, and predicting epistasis along evolutionary trajectories, both in microbial cells and single proteins. We focus on simple patterns of global epistasis that emerge in this data, in which the effects of mutations can be predicted by a small number of variables. The emergence of these patterns offers promise for efforts to model epistasis and predict evolution.

Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.

Loss of gene function is common throughout evolution, even though it often leads to reduced fitness. In this study, we systematically evaluated how an organism adapts after deleting genes that are important for growth under oxidative stress. By evolving, sequencing, and phenotyping over 200 yeast lineages, we found that gene loss can enhance an organism’s capacity to evolve and adapt. Although gene loss often led to an immediate decrease in fitness, many mutants rapidly acquired suppressor mutations that restored fitness. Depending on the strain’s genotype, some ultimately even attained higher fitness levels than similarly adapted wild-type cells. Further, cells with deletions in different modules of the genetic network followed distinct and predictable mutational trajectories. Finally, losing highly connected genes increased evolvability by facilitating the emergence of a more diverse array of phenotypes after adaptation. Together, our findings show that loss of specific parts of a genetic network can facilitate adaptation by opening alternative evolutionary paths.

Environmentally induced phenotypes have been proposed to initiate and bias adaptive evolutionary change toward particular directions. The potential for this to happen depends in part on how well plastic responses are aligned with the additive genetic variance and covariance in traits. Usingmeta-analysis,we demonstrate that plastic responses to novel environments tend to occur along phenotype dimensions that harbor substantial amounts of additive genetic variation. This suggests that selection for or against environmentally induced phenotypes typically will be effective. One interpretation of the alignment between the direction of plasticity and the main axis of additive genetic variation is that developmental systems tend to respond to environmental novelty as they do to genetic mutation. This makes it challenging to distinguish if the direction of evolution is biased by plasticity or genetic “constraint.” Our results therefore highlight a need for new theoretical and empirical approaches to address the role of plasticity in evolution.