Evolution in Action: Past, Present and Future

Life often follows a peculiar and winding path. This paper connects some events in my life with how I got to know Erik Goodman and how the BEACON Center for the Study of Evolution in Action came into being. Some of these events are from memory, while others were recorded in emails. Of course, BEACON has had many participants and so there are many narrative threads, which together are woven into the tapestry of BEACON and our lives.

Under the direction of Erik Goodman, BEACON has been a remarkable success. It has been a fabulous boon to those of us whose research interests are constantly evolving. Here I explain how Erik’s guidance of BEACON facilitated the professional development of many graduate students and post-docs in my lab, and allowed me to reinvent myself as a scientist multiple times during the past decade.

The BEACON Center for the Study of Evolution in Action is a multisite, interdisciplinary science and technology center funded by the National Science Foundation. BEACON effectively institutionalized its two overarching diversity goals of 1) ensuring that diversity is represented as an inclusive and connecting thread throughout the consortium, and 2) exceeded national norms for diversity at all levels across the consortium. As BEACON approaches its sunsetting 10th year, there is great pride in our sustained diversity outcomes and accomplishments. It was very important for us to capture our shared core values, implementation strategies, administrative infrastructure, and programmatic outcomes so that subsequent multisite partnership can use our strategies and tools as a blueprint.

Funders want people to work together, yet successful collaborations take more than wanting it to work out. Our ongoing organizational evaluation of the Bio/- computational Evolution in Action CONsortium (BEACON – a multi-institutional NSF-funded science and technology center) found that creating a successful multiinstitutional research collaborative takes forethought and ongoing effort in order to thrive and achieve its mission and goals. A strategic and servant leader is necessary for a successful collaboration. The leader needs to be respected in the field; be able to work effectively with and motivate key stakeholders, faculty, and students; work collaboratively and with a coalition; recognize the importance of organizational evaluation; and be respected and trusted for his leadership abilities. This article articulates the key attributes of Dr. Erik Goodman’s leadership of the BEACON Center Life’s natural tendency is to organize. Life organizes into greater levels of complexity to support more diversity and greater sustainability [12, p.3]

This chapter reports on my graduate study experience, from having little background in computational biology and bioinformatics to a PhD studying hostmicrobe interactions and the microbiome. As a first-generation Latina college student, the financial, academic, and professional support provided by the BEACON Center for the Study of Evolution in Action was instrumental in allowing my journey and growth in the PhD program. BEACON is an excellent example of a Center that has programming initiatives that truly support students and do increase diversity and inclusion.

This is an edited transcript of my speech on the occasion of Erik Goodman’s 75th birthday celebration at BEACON Congress 2018.

Our inability to predict how populations of cells will evolve is a fundamental challenge to human health and biological engineering. In medicine, one would like to predict and thwart, or at least have time to adequately prepare for potentially catastrophic events such as the emergence of new pathogens, the spread of drug resistance, and the progression of chronic infections and cancers. In bioengineering, one would like to stop, or at least delay, evolution that inactivates a designed function, in order to make genetic engineering and synthetic biology more reliable and efficient. On a larger scale, one would also like to predict when the presence of recombinant DNA or a certain species might pose a threat to nature or civilization if it has the potential to evolve to become harmful.

Experimental studies of evolution using microbes have a long tradition, and these studies have increased greatly in number and scope in recent decades. Most such experiments have been short in duration, typically running for weeks or months. A venerable exception, the long-term evolution experiment (LTEE) with Escherichia coli has continued for 30 years and 70,000 bacterial generations. The LTEE has become one of the cornerstones of the field of experimental evolution, in general, and the BEACON Center for the Study of Evolution in Action, in particular. Science laboratories and experiments usually have finite lifespans, but we hope that the LTEE can continue far into the future. There are practical issues associated with maintaining such a long-term experiment. One issue, which we address here, is whether key measurements made at one time and place are reproducible, within reasonable limits, at other times and places. This issue comes to the forefront when one considers moving an experiment like the LTEE from one lab to another. To that end, the Barrick lab at The University of Texas at Austin, measured the fitness values of samples from the 12 LTEE populations at 2,000, 10,000, and 50,000 generations and compared the new data to data previously obtained at Michigan State University. On balance, the datasets agree very well. More generally, this finding shows the value of simplicity in experimental design, such as using a chemically defined growth medium and appropriately storing samples from microbiological experiments. Even so, one must be vigilant in checking assumptions and procedures given the potential for uncontrolled factors (e.g., water quality) to affect outcomes. This vigilance is perhaps especially important for a trait like fitness, which integrates all aspects of organismal performance and may therefore be sensitive to any number of subtle environmental influences.

There has been an increased usage of metallic antimicrobial materials to control pathogenic and multi-drug resistant bacteria, yet there is a corresponding need to know if this usage may lead to genetic adaptations that produce even more dangerous bacterial strains. In this paper we examine important recent results from the literature as well as report results from a series of our own studies. In that work, we utilized experimental evolution to produce strains of Escherichia coli K-12 MG1655 resistant to silver (Ag+), excess copper (Cu2+), excess iron (II, III), and the iron analog gallium (Ga3+). Silver and gallium are toxic to bacteria, whereas iron and copper are essential micronutrients that can be toxic in excess amounts. In all cases, the evolution of metal resistance was rapid and resulted in pleiotropic effects that included resistance to other metals as well as traditional antibiotics. Genomic analysis identified mutations in several genes associated with metal resistance, falling in several broad classes: genes that prevent entry of metal into the cell, genes involved in the energy-dependent efflux of metal out of the cell, genes associated with ROS-induced membrane damage, and genes associated with transcription.

Evolution innovates by repurposing existing genetic elements to produce new functions. However, the range of new functions and traits this evolutionary tinkering can produce is limited to those that are supported and enabled by the rest of the genome. The full complement of genes in a genome required for a novel trait to manifest constitutes the trait’s “deep” genetic basis. The deep genetic basis of novel traits can be very difficult to determine under most circumstances, leaving it understudied despite its critical importance. Novel traits that arise during highly tractable microbial evolution experiments present opportunities to correct this deficit. One such novel trait is aerobic growth on citrate (Cit+ ), which evolved in one of twelve populations in the Long-Term Evolution Experiment with Escherichia coli (LTEE). We sought to uncover the deep genetic basis of this trait by transforming 3,985 single gene knockout mutants from the Keio collection with a plasmid that can confer aerobic growth on citrate. In our preliminary screen, we identified 111 genes putatively necessary for expression of the Cit+ trait. Of these, ∼ 32% are involved in core metabolic pathways, including the TCA and glycolysis pathways. Another ∼ 22% encode a variety of transporter proteins. The remaining genes are either of unknown function or uncertain involvement with citrate metabolism. Our work demonstrates how novel traits that are built upon pre-existing functions can depend on the activity of a large number of genes, hinting at an unappreciated level of complexity in the evolution of relatively simple new functions.

Fitness tradeoffs play important roles in the evolution of organisms and communities. One such tradeoff often occurs when bacteria become resistant to phage at the cost of reduced competitiveness for resources. Quantifying the cost of phage resistance has frequently relied on measuring specific traits of interest to industrial applications. In an evolutionary context, however, fitness encompasses the effects of all traits relevant to an organism’s survival and reproductive success in a particular environment. Therefore, measurements of the net reproduction and survival of alternative genotypes offer greater power for predicting the fate of different genotypes in complex and dynamic communities. In this study, we measured the fitness of experimentally evolved, λ-resistant Escherichia coli isolates relative to their sensitive progenitor in both the absence and presence of phage. We also characterized certain phage-related phenotypes and obtained complete genome sequences of the bacteria. All of the evolved bacteria exhibited tradeoffs, such that they were more fit than the ancestor in the presence of phage, but less fit than the ancestor in the absence of phage. The fitness benefit of evolved resistance in the presence of abundant phage was generally much larger than the cost, and these effects appear to be driven by only a few resistance mutations. This asymmetrical benefit-tocost relation is consistent with the observation that sensitive cells did not persist in the experimental communities. Quantifying fitness effects in both the presence and absence of phage may thus provide a useful approach for predicting evolutionary outcomes in both natural and engineered microbial communities.

Experimental evolution studies offer the possibility to examine whether closely related populations converge versus diverge in phenotype and genotype, when challenged to adapt under identical selection pressures. Human rhinoviruses (RV) are largely responsible for common cold illnesses, but little is known on whether different strains of these RNA viruses tend to evolve similarly when cultured on cells in laboratory tissue culture. Here we compared and contrasted evolution of two RV-A serotype strains (RV-1A, RV-1B) grown for ∼25 passages on mouse-derived LA-4 cells, expanding on a previous study concerning temperaturedependent innate immunity in mouse-adapted rhinovirus strains. Results showed faster adaptation of the population founded by RV-1B, consistent with classic theory predicting more rapid improvement in populations poorly adapted to their environment. Moreover, we observed increased molecular divergence between the two lineages as they adapted to mouse cells, demonstrated by selection targeting different viral capsid genes and different substitutions in affected viral replication genes, across the two populations. Our findings identify the genetic changes associated with human rhinovirus adaptation to a novel mouse host, which furthers the understanding of loci contributing to RV host jumps in mammals and efforts to develop mice as useful animal models for studying RV infection and pathogenicity.

This study uses multidimensional, information vectors of amino acid composition and codon usage to characterize natural variation in a wide, phylogenetic spectrum of bacteria, and probes the limits of natural variation. These vectors are used to construct a phase space of natural variation that represents all possible states the evolving genes or sets of genes occupy within a viable organism.

Although extinction is ubiquitous throughout the history of life, the factors that drive extinction events are often difficult to decipher. Most studies of extinction focus on inferring causal factors from past extinction events, but these studies are constrained by our inability to observe extinction events as they occur. Here, we use digital evolution to avoid these constraints and study “extinction in action”. We examine the genetic mechanisms driving the relationship between genome size and population extinction. We find that genome expansions enhance extinction risk through two genetic mechanisms that increase a population’s lethal mutational burden: an increased lethal mutation rate and an increased likelihood of stochastic reproduction errors. This result, contrary to the expectation that genome expansions should buffer mutational effects, suggests a role for epistasis in driving extinction. We discuss biological analogues of these digital “genetic” mechanisms and how large genome size may inform which natural populations are at an increased risk of extinction.

Mammals that occupy divergent temporal niches are active in worlds that provide different sensory cues. In particular, activity during the day affords greater opportunity to use light to obtain information. But to capitalize on sensory cues, an animal must have a brain that can process them, and brain tissue is costly. Here, we present preliminary data evaluating the hypothesis that there have been tradeoffs in investment among sensory regions of the brain at evolutionary transitions from one temporal niche to another. We compare the sizes of five brain structures (olfactory bulb, superior colliculus, lateral geniculate nucleus, inferior colliculus, medial geniculate nucleus) in four rodent species (southern flying squirrel and red squirrel, suborder Sciuromorpha; Australian bush rat and Nile grass rat, suborder Myomorpha), representing at least two independent evolutionary transitions from one temporal niche to another. Investment in olfactory bulbs and one visual structure (superior colliculus) was consistent with an evolutionary tradeoff, i.e. nocturnal representatives of each clade had a larger olfactory bulb and smaller superior colliculus. The other visual structure (lateral geniculate nucleus) was larger in the diur-nal than the nocturnal rat, but did not differ significantly between the two squirrels. Moreover, the two auditory structures were larger in the nocturnal than the diurnal sciuromorph, but smaller in the nocturnal than the diurnal myomorph. We evaluate these data with respect to the hypothesis that temporal niche transitions have shifted the value of sensory brain regions and discuss what they suggest more generally about the mosaic nature of sensory brain evolution.

There is growing interest in the alteration of host behaviors by parasites, yet crucial gaps remain in our understanding of its ecological and evolutionary significance. Here, we present the first evidence that the enhanced boldness of infected intermediate hosts of Toxoplasma gondii can increase their risk of mortality by the parasite’s definitive feline hosts. In a long-term study of hyenas in Kenya’s Masai Mara region, we found that 65% of hyenas were seropositive for T. gondii in ELISA IgG assays. Seropositive hyenas approached lions more closely than uninfected counterparts, and also showed longer latencies to approach a simulated conspecific territorial intruder. Lastly, although not significant, the ratio of mortalities caused by lions (vs. other sources) was higher for hyenas that were infected by T. gondii. These results accord with a long-standing hypothesis that the manipulation of host boldness and/or ailurophilia evolved to enhance disease transmission. Since hyenas are rarely consumed by lions, however, elevating their boldness toward lions may not be adaptive for T. gondii. Instead, it may reflect “collateral manipulation” that evolved to influence homologous mechanisms underlying behaviors of alternative hosts (e.g. rodents). This model is often invoked to explain T. gondii’s many effects in humans, but is virtually unexplored in natural settings. For T. gondii, these effects could feasibly impact both behavior and fitness in a vast array, and significant proportion, of earth’s mammals and birds. In addition to characterizing behavioral covariates of infection, we examined spatial and temporal patterns of T. gondii prevalence within the Mara landscape. Contrary to our predictions, disease prevalence did not differ 1) at a protected vs. disturbed locality, or 2) over three decades of increasing human activity within the disturbed locality.

Predator-prey coevolution is commonly thought to result in reciprocal arms races that produce increasingly extreme and complex traits. However, such directional change is not inevitable. Here, we provide evidence for a previously undemonstrated dynamic that we call ’strategy chases,’ wherein populations explore strategies with similar levels of complexity, but differing behaviorally. Indeed, in populations of evolving digital organisms, as prey evolved more effective predatoravoidance strategies, they explored a wider range of behavioral strategies in addition to exhibiting increased levels of behavioral complexity. Furthermore, coevolved prey became more adept in foraging, evidently through coopting components of explored sense-and-flee avoidance strategies into sense-and-retrieve foraging strategies. Specifically, we demonstrate that coevolution induced non-escalating exploration of behavioral space, corresponding with significant evolutionary advancements, including increasingly intelligent behavioral strategies.

The hologenome concept of evolution posits that animals and their symbiotic microbes are emergent individuals, or holobionts, exhibiting synergistic phenotypes that are subject to evolutionary forces. Its premises are that interactions between animals and their microbes affect the fitness of holobionts, in both beneficial and deleterious ways, and that microbes and their functional genes can persist across animal host generations with fidelity. Covariance between the host genome and the microbiome can thus be maintained and holobiont phenotypes encoded by the microbiome can be subject to selection and drift within holobiont populations. Animal behavior researchers have historically underappreciated the beneficial effects of symbiotic microbes on animals’ behavioral phenotypes, but this is changing. Symbiotic microbes can protect their host animals from predators, increase their foraging efficiencies and reproductive outputs, and mediate their chemical communication systems. The objectives of this chapter are to introduce the hologenome concept of evolution and, within the framework of the concept’s premises, to present highlights of the current understanding of how symbiotic microbes contribute to animals’ behavioral phenotypes and how animals facilitate transmission of beneficial microbes to their offspring and kin. The chapter concludes with a discussion of how behavioral ecologists, in particular, are well-positioned to evaluate the explanatory value of host-microbial evolutionary models, such as the hologenome concept.

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques are also well positioned to take advantage of large-scale parallel computing resources, making creative AI through evolutionary computation the likely “next deep learning”.

Predicting ecological and evolutionary dynamics is challenging because the phenomena of interest emerge from complex nonlinear interactions between genomes, organisms, and environments. Complexity theory predicts that small changes in a basal element of an ecosystem can impact higher-order features such as population dynamics and biodiversity. Here we use a simple two-species laboratory system to demonstrate how slight alterations to the environment can have cascading effects on the ecology and evolution of that system. We cultured the bacterium Escherichia coli and a virus, phage λ, together in a carbon-limited medium. We varied the carbon source by supplying one of three similar sugars: glucose, maltose, or maltotriose. These sugars were chosen because we predicted they would impose varying degrees of constraint on the potential for the bacteria to evolve resistance to the phage. The sugars have different routes into the cell: both maltodextrins rely on the outer-membrane pore LamB, whereas glucose does not. LamB also serves as the receptor for λ attachment to the cell surface, and mutations that alter its structure or reduce its expression can confer resistance to λ. By varying the sugar and thereby the bacteria’s reliance on LamB, we predicted they would evolve different types of resistance and engage in different coevolutionary trajectories with λ. We saw even more striking effects than expected. This simple resource manipulation caused differences in the bacteria’s cost of resistance, which in turn affected population dynamics, community composition, coexistence, and coevolution. This cascade has important implications for predicting ecology and evolution. On the one hand, it reveals that even subtle environmental differences can have large and complex effects, making predictions difficult. On the other hand, important features of the environment (here, the specific limiting resource) can sometimes be identified a priori given sufficient knowledge of the molecular biology and physiology of the organisms.

Parasites exert strong selective pressure on their hosts, and many hosts can evolve rapidly in response. As such, host-parasite interactions have a special place in the study of contemporary evolution. However, these interactions are often considered in isolation from the ecological contexts in which they occur. Here we review different ways in which the ecological context of host-parasite interactions can modulate their evolutionary outcomes in important and sometimes unexpected ways. Specifically, we highlight how predation, competition, and abiotic factors change the outcome of contemporary evolution for both hosts and parasites. In doing so, we focus on insights gained from the Daphnia-microparasite system. This system has emerged as a model system for understanding the ecology and evolution of host-parasite interactions, and has provided important insights into how ecological context influences contemporary evolution.

The idea of stable biotic communities that are resilient to disturbance and invasion, and in which evolutionary change may be impeded by ecological interactions, has garnered considerable attention in both theoretical ecology and paleobiology. In the latter field, stable communities are often discussed in terms of “coordinated stasis”, where entire biotic communities may display the canonical evolutionary stasis of punctuated equilibria for significant periods of time. A prevailing hypothesis that has been advanced to explain coordinated stasis is habitat tracking, in which species evolve little as long as they can migrate to regions of habitat containing their optimal living conditions. In this contribution, we describe how we have modified Avida, a proven digital evolution platform, for investigating the habitat tracking hypothesis in a rigorous, experimental manner. We model habitats consisting of spatially localized fields of resources and fixed landmarks, and modified Avida to give its digital organisms capabilities for movement, vision, resource sensing, and resource consumption. Our preliminary results show that while the movement and ecological subsystems we implemented each produce positive outcomes in isolation, they conflict with each other when combined and result in these outcomes occurring infrequently. Nonetheless, the results validate the effectiveness of these additions and set the stage for further habitat relocation experiments, with clear predictions about expected responses.

The astonishing complexity of the world around us is the result of major transitions in evolution where lower-level entities unite to form a higher-level unit: living and reproducing as one. These transitions give rise to many questions within evolutionary biology, but can prove challenging to study due to their rare occurrence in nature. Here, we describe a digital evolution approach where cells, organisms, and worlds all exist within the framework of a computer and as such have rapid generation times and are amenable to experimental control, replaying key events, and precise data tracking. Using this approach we describe our previous experiments exploring fraternal major transitions in evolution—transitions that occur when identical lower-level units (e.g., cells) remain together as one higher-level unit (e.g., a multicellular organism). We have performed experiments to test key hypotheses regarding the formation of higher-level units and the reproductive and task-based division of labor that can evolve once these units are in place.We then describe a series of on-going studies that explore hypotheses regarding the forces that prevent higher-level units from reverting to their lower-level origins, how plasticity may predispose the evolution of division of labor, and how egalitarian transitions (which occur when different lower-level units come together) may occur.

One of the main success stories in the evolutionary computation (EC) field is the use of EC framework to solve multi-criterion optimization problems. These problems give rise to a set of trade-off Pareto-optimal solutions, instead of a single optimal solution; hence a population-based EC framework is a natural choice for solving them. Starting in the early nineties with a few parameter-dependent algorithms, the research and application of evolutionary multi-criterion optimization (EMO) algorithms has become a field of its own, by attracting mathematicians, computer scientists, engineers, economists, managers, and entrepreneurs. In this chapter, we provide a chronological account of key contributions which kept the field alive, useful, and vibrant. A bibliometric study of published materials on the topic is also provided to paint a picture of the rise and the popularity of the field.

In this chapter we reflect about evolutionary art, past and present, and how best to evaluate it for positioning in the art world. A personal journey that went through Michigan State University’s Garage Lab, led to a series of projects at the intersection of arts and science. This has enabled me to reflect on the synergies between both areas from the point of view of evolutionary computation.So far, researchers have discussed how to design appropriate fitness functions for aesthetic evaluations, and have resorted to interaction to improve quality evaluation. We suggest that both the public and art critics, represented by experts, scholars, museums, art galleries and international art contests, be included in the evaluation process of evolutionary art. Only then, will art created by evolutionary processes have a chance to become a new trend that competes on an equal footing with other types of art.

The automation of program repair can be coached in terms of search algorithms. Repair templates derived from common bug-fix patterns can be used to determine a promising search space with potentially many correct patches, a space that can be effectively explored by GP methods. Here we propose a new repair system, ARJA-p, extended from our earlier ARJA system of bug repair for JAVA, which integrates and enhances the performance of the first approach that combines repair templates and EC, PAR. Empirical results on 224 real bugs in Defects4J show that ARJA-p outperforms state-of-the-art repair approaches by a large margin, both in terms of the number of bugs fixed and of their correctness. Specifically, ARJA-p can increase the number of fixed bugs in Defects4J by 29.2% (from 65 to 84) and the number of correctly fixed bugs by 42.3% (from 26 to 37).

The design of computational arms races can draw upon the compelling inspiration of biological arms races. To study cyber security attack-defense dynamics, we have abstracted a description of biological adversarial ecosystems to design an adversarial computational system. The system has elements and processes with abstracted biological analogs. It centers on engagements. Engagements feature adversarial actors (predator/attacker, prey/defender) competing with conflicting objectives culminating in a measurable performance-based outcome. Adversarial dynamics are controlled by coevolution, which selects for better adversaries over multiple engagements using aggregate engagement performance as fitness. Altogether, this system abstracted from nature is capable of population-based, arms race dynamics arising from interacting, evolving adversaries.

A key challenge in Evolutionary Computation is fitness landscape design. While the objective of the optimization process is often predefined, the actual fitness function, computational substrate, mutation scheme, and selection function must often be chosen by the experimenter. These choices are recognized to possibly have a large impact on experimental results. Here we investigate implementation differences of the encoding method and elaborate on its potential impact.

Engineering optimization problems are challenging to solve mainly due to their numerical modeling and analysis complexities. This chapter deals with the efficient use of surrogate model-driven evolutionary algorithms, built hierarchically for the solution of large-scale computation intensive optimization problems. In most optimization problems, the majority of computation is involved in repetitive function calls to evaluate the system response/bahaviour under consideration. The quality solutions depends on the system response estimation, and in most cases high fidelity models are used to get accurate results. Conventional evolutionary algorithms require a great number of such high fidelity function calls. Here, we use low cost surrogate models or metamodels, which approximate the original model mathematically, but significantly reduce the computation cost for a desired accuracy level. The surrogate model training requires a small amount of evaluations of the original model at support points. The hierarchical surrogate model-based PSO algorithms we propose are tested on a range of large-scale design optimization problems and compared with other well-known surrogate modeling techniques.

This paper gives a short review on mechatronic design automation (MDA) whose optimization method is mainly based on evolutionary computation techniques. The recent progress and research results of MDA are summarized systematically, and the challenges and future research directions in MDA are also discussed. The concept of MDA is introduced first, research results and potential challenges of MDA are analyzed. Then future research directions, focusing on constrained multiobjective optimization, surrogate-assisted constrained multi-objective optimization, and design automation by integrating constrained multi-objective evolutionary computation and knowledge extraction, are discussed. Finally, we suggest that MDA has great potential, and may be the next big technology wave after electronic design automation (EDA).

The use of genetic variation (DNA markers) has become widespread for prediction of genetic merit in animal and plant breeding and it is gaining momentum as a prognostic tool for propensity to disease in human medicine. Although conceptually straightforward, genomic prediction is a very challenging problem. Genotyping organisms and recording phenotypic traits are time consuming and expensive. Resultant datasets often have many more features (markers) than samples (organisms). Therefore, models attempting to estimate the effects of markers often suffer from overfitting due to the curse of dimensionality. Feature selection is desirable in this setting to remove markers that do not appreciably affect the trait being predicted and amount to statistical noise.We present a differential evolution system for feature selection in genomic prediction problems and demonstrate its performance on simulated data. Code is available at: https://github.com/ianwhale/tblup .

Public acceptance of evolution is conspicuously low in the United States. This is especially true in the American Southeast, where the famous “Scopes Monkey Trial” made the religious objections of rural Tennesseans to Darwin’s theory famous. In this piece I argue that disbelief in evolution in the Southeast is caused more by social factors than by scientific ones, and I present some efforts that I have successfully used to side-step these social issues in order to effectively teach evolutionary biology to diverse college students in Alabama. I close by calling on other educators to work to defuse the religious, political, and social minefields that separate academic biologists and the public in order to be more effective communicators and teachers of evolution.

Few things are more important to a research career than discovery. A great idea is important and new. It might lead to the founding of a new field, or a new approach to an existing one, and sprout hundreds of additional studies. Where do these ideas come from? The answer to this question lies in the general field of creativity research. Here I discuss how creativity might be enhanced in ten steps. They involve being motivated to be creative, to learn creativity techniques, to interact with others outside your core group, to foster eureka moments, to recognize good new ideas when you see them, to work with others, to encourage creativity in others, to give feedback in a kind way, to defer ownership of ideas, and to learn science improvisation. The references in these sections give a peek into the growing field of creativity studies. Greater attention to and understanding of the sources of creative insight will help lead us to that magic idea that is new and important.

Genetics and evolution are interconnected topics — evolutionary change requires inheritance and correspondingly, genetic variation is required for selection to have any impact on a population. However, misconceptions and naive ideas of both genetic and evolutionary concepts can fundamentally impact a student’s understanding of biology. It is therefore important to understand what information students obtain in various courses at the undergraduate level, and how knowledge of concepts in one course might impact learning in another course. This is particularly important with respect to genetics concepts, as Genetics courses are often a prerequisite to Evolution courses and serve frequently as students’ introduction to the basic concepts that underlie evolution. This study compared student performance related to key genetics concepts after taking both Fundamental Genetics and Evolution courses to taking Fundamental Genetics alone and tracked student performance as they progressed through the Genetics-Evolution course sequence. We created a 16-question assessment, developed from published literature on these topics, and administered the survey at three timepoints: the end of Fundamental Genetics, the beginning of Evolution and again at the end of the Evolution course. Our data suggest students do complete Fundamental Genetics with a few misconceptions related to genetic information pertinent to evolution, and that these concepts are varyingly corrected by taking Evolution. This research highlights the advantages of both tracking and comparing students as they progress through a Genetics-to-Evolution course sequence, particularly with respect to how faculty can leverage course sequencing to improve student performance.

The Scientific Virtues Toolbox is a novel scientific virtue-based workshop model for responsible conduct of research (RCR) training. This paper gives a brief overview of how Pennock’s vocational virtue theory, which had previously been delivered in courses, was transformed into discussion-based RCR workshops using the Toolbox structured dialogue method. The interdisciplinary BEACON Center, which combined biologists, computer scientists, and engineers, and which aimed to model a culture of excellence, ethics, and inclusion, proved to be an ideal environment to develop and test this approach to the cultivation of scientific character. The paper describes the guided-dialogue structure of the workshops, the nature of the discussion prompts, the pilot assessments carried out, and how the workshops are now evolving beyond their scientific origin.

To investigate how instructors’ technological pedagogical content knowledge (TPACK) influences the way they implement novel educational technologies and how this influences students’ affective responses to the technology, we looked at how instructors with varying amounts of TPACK with regard to a specific educational tool—the digital evolution platform Avida-ED—implemented it in their classrooms. We then compared the nature of these implementations to student affective outcomes as measured by a post-implementation student survey. We found that the degree of instructor expertise influences implementation decisions, and that these decisions in turn influence student affect. Effective implementation of new educational technologies requires significant pedagogical knowledge, and warrants opportunities for training and professional development with regard to those technologies.

This chapter outlines the difficulty and importance of tackling human evolution in the classroom, and the different demands this places on the instructor. It discusses lessons for teaching evolution in action, specifically in the context of human evolution and population genetics. These lessons include explicit hands-on activities for the students in analyzing and interpreting human genetic data, and addressing the information flow from scientific journal articles to popular presentation in the media. This content was implemented in a course designed for graduate students though with the occasional advanced undergraduate student with background in STEM fields other than biology. The chapter finally reflect on what the students gain from these lessons, and what limitations remain.

This contribution summarizes the academic biography of the founding director of the NSF-funded BEACON Center for the Study of Evolution in Action, Dr. Erik D. Goodman, in a first person narrative.