Runaway evolution from male-male competition

Understanding why and how elaborated traits evolve remains a fascination and a challenge. Darwin proposed both male-male competition and female mate choice as explanations for elaboration because such traits are often mediators of social interactions that govern access to mates. Although we have robust evolutionary quantitative genetic models for how mate choice can lead to runaway evolution, we lack an equivalent framework for understanding how male-male competition can drive extreme elaboration of traits. Here, we integrate the logic of optimality models into the quantitative genetic framework of interacting phenotypes to fill this gap. We assume that males modulate their aggression based on the relative size of a trait that signals willingness and ability to fight and identify conditions where the signal undergoes rapid and exponential evolution. Males receive fitness benefits from winning contests, but they may accrue fitness costs due to threats imposed by their opponent. This cost leads to a force of social selection that accelerates as the signaling trait is elaborated, which may cause runaway evolution of the signal. Even when a runaway is checked by natural selection, we find that signaling traits evolving by male-male competition can be elaborated well beyond their naturally selected optimum. Our model identifies simple conditions generating feedback between the behavioral and morphological traits mediating male-male competition, providing clear testable predictions. We conclude that, like the well-characterized case of female mate choice, male-male competition can provide a coevolving source of selection that can drive a runaway process resulting in evolution of elaborate traits.

An Optimal Lysis Time Maximizes Bacteriophage Fitness in Quasi-continuous Culture

ABSTRACTOptimality models have a checkered history in evolutionary biology. While optimality models have been successful in providing valuable insight into the evolution of a wide variety of biological traits, a common objection is that optimality models are overly simplistic and ignore organismal genetics. We revisit evolutionary optimization in the context of a major bacteriophage life history trait, lysis time. Lysis time refers to the period spanning phage infection of a host cell and its lysis, whereupon phage progeny are released. Lysis time, therefore, directly determines phage fecundity assuming progeny assembly rate is maximized. Noting that previous tests of lysis time optimality rely on batch culture, we implemented a quasi-steady state system to observe productivity of a panel of isogenic phage λ mutants differing in lysis time. We report that λ phage productivity in our experiments is maximized around an optimal lysis time of 65 min, which is the lysis time of the λ “wildtype” strain. We discuss this finding in light of recent results that lysis time variation is also minimized in the λ “wildtype” strain.

Is Amphistomy an Adaptation to High Light? Optimality Models of Stomatal Traits along Light Gradients

Stomata regulate the supply of CO2 for photosynthesis and the rate of water loss out of the leaf. The presence of stomata on both leaf surfaces, termed amphistomy, increases photosynthetic rate, is common in plants from high light habitats, and rare otherwise. In this study I use optimality models based on leaf energy budget and photosynthetic models to ask why amphistomy is common in high light habitats. I developed an R package leafoptimizer to solve for stomatal traits that optimally balance carbon gain with water loss in a given environment. The model predicts that amphistomy is common in high light because its marginal effect on carbon gain is greater than in the shade, but only if the costs of amphistomy are also lower under high light than in the shade. More generally, covariation between costs and benefits may explain why stomatal and other traits form discrete phenotypic clusters.

Selective predators and responsive prey

Predators feed on a variety of prey and this has important consequences for both predator and prey. This chapter introduces optimal foraging theory as a way to understand why predators prefer some prey types over others and discusses the evidence for adaptive diet choice in nature. Simple optimality models are used to understand how predators make decisions about where to feed (habitat choice) and how long to stay in a prey patch (“giving-up-time”). The non-lethal or non-consumptive effects of predators can be as important as their direct lethal effects. Discussed are examples of how prey respond to the threat of predation (the “ecology of fear”) by changing their behaviors, morphologies, physiologies, and life histories. The chapter concludes with an examination of the relative importance of predator consumptive and non-consumptive effects.

Is amphistomy an adaptation to high light? Optimality models of stomatal traits along light gradients

Stomata regulate the supply of CO2 for photosynthesis and the rate of water loss out of the leaf. The presence of stomata on both leaf surfaces, termed amphistomy, increases photosynthetic rate, is common in plants from high light habitats, and rare otherwise. In this study I use optimality models based on leaf energy budget and photosynthetic models to ask why amphistomy is common in high light habitats. I developed an R package leafoptimizer to solve for stomatal traits that optimally balance carbon gain with water loss in a given environment. The model predicts that amphistomy is common in high light because its marginal effect on carbon gain is greater than in the shade, but only if the costs of amphistomy are also lower under high light than in the shade. More generally, covariation between costs and benefits may explain why stomatal and other traits form discrete phenotypic clusters.

Empirical patterns of environmental variation favor the evolution of adaptive transgenerational plasticity

Effects of parental environment on offspring traits have been well known for decades. Interest in this transgenerational form of phenotypic plasticity has recently surged due to advances in our understanding of its mechanistic basis. Theoretical research has simultaneously advanced by predicting the environmental conditions that should favor the adaptive evolution of transgenerational plasticity. Yet whether such conditions actually exist in nature remains largely unexplored. Here, using long-term climate data, we modeled optimal levels of transgenerational plasticity for an organism with a one-year life cycle at a spatial resolution of 4km2 across the continental US. Both annual temperature and precipitation levels were often autocorrelated, but the strength and direction of these autocorrelations varied considerably across the continental US and even among nearby sites. When present, such environmental autocorrelations render offspring environments statistically predictable based on the parental environment, a key condition for the adaptive evolution of transgenerational plasticity. Results of our optimality models were consistent with this prediction: high levels of transgenerational plasticity were favored at sites with strong environmental autocorrelations, and little-to-no transgenerational plasticity was favored at sites with weak or non-existent autocorrelations. These results are among the first to show that natural patterns of environmental variation favor the evolution of adaptive transgenerational plasticity. Furthermore, these findings suggest that transgenerational plasticity is highly variable in nature, depending on site-specific patterns of environmental variation.