Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations.

The oscillatory Min system of Escherichia coli defines the cell division plane by regulating the site of FtsZ-ring formation and represents one of the best-understood examples of emergent protein self-organization in nature. The oscillatory patterns of the Min-system proteins MinC, MinD and MinE (MinCDE) are strongly dependent on the geometry of membranes they bind. Complex internal membranes within cyanobacteria could disrupt this self-organization by sterically occluding or sequestering MinCDE from the plasma membrane. Here, it was shown that the Min system in the cyanobacterium Synechococcus elongatus PCC 7942 oscillates from pole-to-pole despite the potential spatial constraints imposed by their extensive thylakoid network. Moreover, reaction-diffusion simulations predict robust oscillations in modeled cyanobacterial cells provided that thylakoid network permeability is maintained to facilitate diffusion, and suggest that Min proteins require preferential affinity for the plasma membrane over thylakoids to correctly position the FtsZ ring. Interestingly, in addition to oscillating, MinC exhibits a midcell localization dependent on MinD and the DivIVA-like protein Cdv3, indicating that two distinct pools of MinC are coordinated in S. elongatus. Our results provide the first direct evidence for Min oscillation outside of E. coli and have broader implications for Min-system function in bacteria and organelles with internal membrane systems.

Genetic Algorithms (GA) are a powerful set of tools for search and optimization that mimic the process of natural selection, and have been used successfully in a wide variety of problems, including evolving neural networks to solve cognitive tasks. Despite their success, GAs sometimes fail to locate the highest peaks of the fitness landscape, in particular if the landscape is rugged and contains multiple peaks. Reaching distant and higher peaks is difficult because valleys need to be crossed, in a process that (at least temporarily) runs against the fitness maximization objective. Here we propose and test a number of information-theoretic (as well as network-based) measures that can be used in conjunction with a fitness maximization objective (so-called "neuro-correlates") to evolve neural controllers for two widely different tasks: a behavioral task that requires information integration, and a cognitive task that requires memory and logic. We find that judiciously chosen neuro-correlates can significantly aid GAs to find the highest peaks.

When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters.

The postdoctoral workforce comprises a growing proportion of the STEM community and plays a vital role in advancing science. Postdoc professional development, however, remains rooted in outdated realities. We propose enhancements to postdoc-centred policies and practices to better align this career stage with contemporary job markets and work life. By facilitating productivity, wellness, and career advancement, the proposed changes will benefit all stakeholders in postdoc success -including research teams, institutions, professional societies, and the scientific community as a whole. To catalyse reform, we outline recommendations for a) skills-based training tailored to the current career landscape, and b) supportive policies and tools outlined in postdoc handbooks. We also invite the ecology and evolution community to lead further progressive reform. Main Text (current word count 2188):Postdoctoral researchers ("postdocs"; Fig. 1A) contribute extensive research, teaching, and service to their supervising faculty, home institutions, and broader scientific communities [1][2][3][4] . In principle, these contributions are rewarded with opportunities to specialize and develop independence. In practice, however, postdocs' progress and well-being are constrained by social, mental, and financial challenges 1,5-7 . Further, the skills and credentials that are prioritized in postdoc positions are misaligned with contemporary job markets (e.g. [8][9][10][11] , Figure 1C). These issues highlight an urgent need for policies and practices that better support a growing postdoctoral workforce. Ultimately, this will benefit all stakeholders in postdoc success --providing ethical and far-reaching returns on time and resource investments [1][2][3][4][5]12 .Below, we describe five goals for enhancing postdoc professional development. We also highlight innovative examples of policies and practices from around the globe. Our recommendations are applicable to many STEM disciplines, but especially relevant to ecology and evolution. Alternative careers in these fields commonly require additional training [13][14][15] , and non-academic paths are often unknown to both postdocs and their mentors. This causes anxiety and reticence for postdocs who, by choice or by necessity, are considering nontraditional careers 1,16,17 . Fortunately, the ecology and evolution community is also poised to lead adaptive reform. Our research targets complex interactions spanning many levels of biological organization. Consequently, our community possesses the tools and perspectives needed for strategic, evidence-based engineering of workplace ecosystems 9 .Goal 1: Align career development with job markets 1,3,38,39 ). More effective mentorship can be facilitated through training, 36 and should be incentivized during hiring, evaluation, and merit-based promotion 40 .