How does genetic architecture affect eco-evolutionary dynamics? A theoretical perspective

Demographic population dynamics, gene flow, and local adaptation may influence each other and lead to coupling of ecological and evolutionary dynamics, especially in species inhabiting fragmented heterogeneous environments. Here, I review long-term research on eco-evolutionary spatial dynamics in the Glanville fritillary butterfly inhabiting a large network of approximately 4,000 meadows in Finland. The metapopulation persists in a balance between frequent local extinctions and recolonizations. The genetic spatial structure as defined by neutral markers is much more coarse-grained than the demographic spatial structure determined by the fragmented habitat, yet small-scale spatial structure has important consequences for the dynamics. I discuss three examples of eco-evolutionary spatial dynamics. (i) Extinction-colonization metapopulation dynamics influence allele frequency changes in the phosphoglucose isomerase (Pgi) gene, which leads to strong associations between genetic variation inPgiand dispersal, recolonization, and local population dynamics. (ii) Inbreeding in local populations increases their risk for extinction, whereas reciprocal effects between inbreeding, population size, and emigration represent likely eco-evolutionary feedbacks. (iii) Genetically determined female oviposition preference for two host plant species exhibits a cline paralleling a gradient in host plant relative abundances, and host plant preference of dispersing females in relation to the host plant composition of habitat patches influences immigration (gene flow) and recolonization (founder events). Eco-evolutionary spatial dynamics in heterogeneous environments may not lead to directional evolutionary changes unless the environment itself changes, but eco-evolutionary dynamics may contribute to the maintenance of genetic variation attributable to fluctuating selection in space and time.

A synthesis is underway between ecology and evolution, partly brought about by the realization that evolutionary change can take place on ecological timescales (Hairston et al., 2005; Whitham et al., 2006; Carroll et al., 2007). This synthesis attempts to understand the dynamic interplay of ecological and evolutionary processes that results from natural or anthropogenic selective forces (Lankau & Strauss, 2007). Moreover, this synthesis represents an integration of several ‘genes to ecosystems’ approaches, including ‘ecological stochiometry’, ‘community genetics’ (Whitham et al., 2006) and ‘niche construction’. United under the framework of ‘eco-evolutionary dynamics’, these ideas seek to link genetic and phenotypic variation to population dynamics, biodiversity and ecosystem function, and place these disciplines in a dynamic evolutionary framework (i.e. understanding the ecological consequences of evolutionary processes and the evolutionary consequences of ecological interactions). This is not an easy endeavor because any such synthesis needs to be broadly multidisciplinary and integrative (Whitham et al., 2006). And yet the potential pay offs are large given that genetic variation across plant and animal systems can have extended consequences at the population, community and ecosystem levels. These consequences can come in the form of the vital rates of survival, reproduction and migration, as well as arthropod and aquatic macroinvertebrate diversity, soil microbial communities, trophic interactions, carbon storage, soil nitrogen availability, dissolved organic nitrogen and production of primary producers (Whitham et al., 2006; Bailey et al., 2009; Ezard et al., 2009; Harmon et al., 2009; Johnson et al., 2009; Palkovacs et al., 2009; Post & Palkovacs, 2009). The effects of genetic or phenotypic variation are not limited to single systems or to ecologically important species (i.e. keystone species, dominant species, foundation species, ecosystem engineers), although these are excellent places to start looking. Instead, genetic variation seems to have effects that are broadly distributed across plant and animal systems - and these effects can be similar in magnitude to those of nonevolutionary ecological variables, such as climate, species invasion and habitat quality (Hairston et al., 2005; Bailey et al., 2009; Ezard et al., 2009; Palkovacs et al., 2009; Post & Palkovacs, 2009).

Ecosystems are complex food webs in which multiple species interact and ecological and evolutionary processes continuously shape populations and communities. Previous studies on eco-evolutionary dynamics have shown that the presence of intraspecific diversity affects community structure and function, and that eco-evolutionary feedback dynamics can be an important driver for its maintenance. Within communities, feedbacks are, however, often indirect, and they can feed back over many generations. Here, we studied eco-evolutionary feedbacks in evolving communities over many generations and compared two-species systems (virus-host and prey-predator) with a more complex three-species system (virus-host-predator). Both indirect density- and trait-mediated effects drove the dynamics in the complex system, where host-virus coevolution facilitated coexistence of predator and virus, and where coexistence, in return, lowered intraspecific diversity of the host population. Furthermore, ecological and evolutionary dynamics were significantly altered in the three-species system compared with the two-species systems. We found that the predator slowed host-virus coevolution in the complex system and that the virus' effect on the overall population dynamics was negligible when the three species coexisted. Overall, we show that a detailed understanding of the mechanism driving eco-evolutionary feedback dynamics is necessary for explaining trait and species diversity in communities, even in communities with only three species.

Although numerous hypotheses exist to explain the overwhelming presence of sexual reproduction across the tree of life, we still cannot explain its prevalence when considering all inherent costs involved. The Red Queen hypothesis states that sex is maintained because it can create novel genotypes with a selective advantage. This occurs when the interactions between species induce frequent environmental change. Here, we investigate whether coevolution and eco-evolutionary feedback dynamics in a predator-prey system allows for indirect selection and maintenance of sexual reproduction in the predator. Combining models and chemostat experiments of a rotifer-algae system we show a continuous feedback between population and trait change along with recurrent shifts from selection by predation and competition for a limited resource. We found that a high propensity for sex was indirectly selected and was maintained in rotifer populations within environments containing these eco-evolutionary dynamics; whereas within environments under constant conditions, predators evolved rapidly to lower levels of sex. Thus, our results indicate that the influence of eco-evolutionary feedback dynamics on the overall evolutionary change has been underestimated.

SummaryThe eco-evolutionary dynamics of microbial communities are predicted to affect both the tempo and trajectory of evolution in constituent species [1]. While community composition determines available niche space, species sorting dynamically alters composition, changing over time the distribution of vacant niches to which species adapt [2], altering evolutionary trajectories [3, 4]. Competition for the same niche can limit evolutionary potential if population size and mutation supply are reduced [5, 6] but, alternatively, could stimulate evolutionary divergence to exploit vacant niches if character displacement results from the coevolution of competitors [7, 8]. Under more complex ecological scenarios, species can create new niches through their exploitation of complex resources, enabling others to adapt to occupy these newly formed niches [9, 10]. Disentangling the drivers of natural selection within such communities is extremely challenging, and it is thus unclear how eco-evolutionary dynamics drive the evolution of constituent taxa. We tracked the metabolic evolution of a focal species during adaptation to wheat straw as a resource both in monoculture and in polycultures wherein on-going eco-evolutionary community dynamics were either permitted or prevented. Species interactions accelerated metabolic evolution. Eco-evolutionary dynamics drove increased use of recalcitrant substrates by the focal species, whereas greater exploitation of readily digested substrate niches created by other species evolved if on-going eco-evolutionary dynamics were prevented. Increased use of recalcitrant substrates was associated with parallel evolution of tctE, encoding a carbon metabolism regulator. Species interactions and species sorting set, respectively, the tempo and trajectory of evolutionary divergence among communities, selecting distinct ecological functions in otherwise equivalent ecosystems.

Adaptive dynamics theory has been devised to account for feedbacks between ecological and evolutionary processes. Doing so opens new dimensions to and raises new challenges about evolutionary rescue. Adaptive dynamics theory predicts that successive trait substitutions driven by eco-evolutionary feedbacks can gradually erode population size or growth rate, thus potentially raising the extinction risk. Even a single trait substitution can suffice to degrade population viability drastically at once and cause 'evolutionary suicide'. In a changing environment, a population may track a viable evolutionary attractor that leads to evolutionary suicide, a phenomenon called 'evolutionary trapping'. Evolutionary trapping and suicide are commonly observed in adaptive dynamics models in which the smooth variation of traits causes catastrophic changes in ecological state. In the face of trapping and suicide, evolutionary rescue requires that the population overcome evolutionary threats generated by the adaptive process itself. Evolutionary repellors play an important role in determining how variation in environmental conditions correlates with the occurrence of evolutionary trapping and suicide, and what evolutionary pathways rescue may follow. In contrast with standard predictions of evolutionary rescue theory, low genetic variation may attenuate the threat of evolutionary suicide and small population sizes may facilitate escape from evolutionary traps.

Chapter Five - Eco-Evolutionary Dynamics: Experiments in a Model System

In proposing his genetic feedback mechanism, David Pimentel was one of the first biologists to argue that the reciprocal interplay of ecological and evolutionary dynamics is an important process regulating population dynamics and ultimately affecting community composition. Although the past decade has seen an increase in research activity on these so-called eco-evolutionary dynamics, there remains a conspicuous lack of compelling natural examples of such feedback. Here we argue that this lack may be due to an inherent difficulty in detecting eco-evolutionary dynamics in nature. By examining models of virulence evolution, host resistance evolution, and antigenic evolution, we show that the influence of evolution on ecological dynamics can often be obscured by other ecological processes that yield similar dynamics. We then show, however, that mechanistic models can be used to navigate this, in Pimentel's words, "devious" course of evolution when effectively combined with empirical data. We argue that these models, improving upon Pimentel's original mathematical models, will therefore play an increasingly important role in identifying more subtle, but possibly ubiquitous, eco-evolutionary dynamics in nature. To highlight the importance of identifying these potentially subtle dynamics in nature, we end by considering our ability to anticipate the effect of population control strategies in the presence of these eco-evolutionary feedbacks.

Johanna Schmitt It is a great pleasure to help honour the 2014 recipient of the Molecular Ecology Prize: Johanna Schmitt, Professor of Evolution and Ecology and of Population Biology at the University of California, Davis. Johanna, or Annie as she is known by friends and colleagues, has had tremendous influence on the field of ecological genetics throughout her career, and her recent work on the genetic basis of adaptation in Arabidopsis thaliana is some of the most ambitious applications of genomic methods to test hypotheses of ecological and evolutionary dynamics. Entering the field of evolutionary genetics and genomics from the field of ecology, she has infused genetic studies of adaptation with a rich and nuanced view of the ecological environment as seen from the perspective of her study organisms. Anyone who has walked in the woods with her will recognize her plants' eye view in her research. As her former postdoc John Stinchcombe observed, ‘one of the things I find remarkable about Annie (among many) is that as the field has transitioned from a few genes or anonymous markers to whole genome level variation, she's never lost her “feel for the organism” or sight of the larger ecological or evolutionary questions that motivated her to go down this path’. Annie majored in Biology at Swarthmore College and continued her PhD in Biology at Stanford University, with Ward Watt as her advisor. There, she wrote her dissertation on the pollination biology of Scenecio and Linanthus, cultivating interests in the population genetic consequences of density-dependent pollination dynamics (e.g. Schmitt 1983a,b). It was during her postdoctoral work at Duke University, with Janis Antonovics (whom she admired as a great female role model, until she met him in person), that she developed her signature methodology of applying genetic designs to clever and complex field experiments. This approach had two important consequences for her own research and for the field of ecological genetics: first, it illustrated how ecological manipulations can be combined with genetic analysis to test evolutionary hypotheses. For example, her work at Duke tested how genetic diversity within local neighbourhoods can influence competitive interactions and adverse effects of herbivores, relating these dynamics to the evolution of sexual reproduction (Schmitt & Antonovics 1986b; Schmitt & Ehrhardt 1987; Kelley et al. 1988). The focus on sexual reproduction also motivated her to distinguish maternal vs. paternal effects on progeny phenotypes, bringing into focus the phenomenon of maternal effects or cross-generational phenotypic plasticity (Antonovics & Schmitt 1986; Schmitt & Antonovics 1986a). Second, this approach illustrated the strong environmental context of the expression of genetically based traits. Her subsequent work, which she continued at her first faculty appointment at Brown University, engaged the evolutionary and ecological consequences of this environment-dependent genetic expression or genotype–environment interaction. She made phenotypic plasticity a central focus of her research programme (Schmitt et al. 1992; Schmitt 1993, 1995). It was this work that pioneered methods for testing the adaptive significance of phenotypic plasticity, both within and across generations (Schmitt 1993, 1997; Wulff et al. 1994, Schmitt et al. 1999). Her combination of environmental manipulations, phenotypic and genetic manipulations, and measurements of environment-dependent natural selection became the gold standard of tests for adaptive plasticity. Her work on shade avoidance responses in Impatiens capensis unambiguously demonstrated adaptive plasticity and documented that not only did phenotypes change in response to environmental conditions, but genetic variances and covariances did as well (Dudley & Schmitt 1996; Schmitt & Dudley 1996; Donohue et al. 2000a,b). That is, the genetic basis of traits under selection, and the genetic relationships among them, depended strongly on the ecological environment they experienced. Annie's work on shade avoidance responses engaged not only the quantitative genetic basis of this complex trait, but the molecular genetic pathways associated with it as well. Shade avoidance—the ability of plants to elongate in response to vegetation shade—was long known to be mediated by the plant photoreceptors, phytochromes (Schmitt & Wulff 1993). During a sabbatical at the University of Leicester, she collaborated with Alex McCormac and Harry Smith to test how the genetic disruption of phytochrome function would alter shade avoidance and fitness. Using transgenic lines of tobacco whose shade avoidance ability had been blocked, and constitutively shade-avoiding mutants of Brassica, they demonstrated a significant fitness disadvantage of inappropriate shade avoidance responses (Schmitt et al. 1995). This was her first work that employed tools of molecular genetics to test ecological hypotheses. While continuing to investigate the quantitative genetic basis of diverse plastic responses to vegetation shade, Annie began to explore other genetic methods to evaluate their genetic architecture. Like other evolutionary geneticists at the time, she discovered the utility of employing natural genetic variation in ecologically important traits to investigate their genetic basis through quantitative trait locus (QTL) mapping. At the time when QTL analysis was just beginning to be broadly applied to identify loci associated with ecologically significant phenotypes, Annie and her associates implemented a highly ambitious QTL study using Arabidopsis thaliana under field conditions to map not only well-defined phenological and morphological phenotypes, but fitness itself. This intense collaborative effort, initially supported by an NSF FIBR grant, was among the very first to map loci associated with fitness under natural conditions in contrasting geographical sites (Weinig et al. 2002, 2003a,b,c). By demonstrating that some genetic loci were associated with fitness only in one location but neutral in another, while other genetic loci were associated with fitness in both locations, but in opposite directions, this study illustrated how QTL analysis could be employed to resolve long-standing issues of trade-offs in adaptation across geographical locations—specifically revealing instances of conditional neutrality and evidence of antagonistic pleiotropy. The success of this research programme spawned a monster, according to the numerous participants of the next major research effort. Encouraged by the success of the two-site field study with numerous recombinant inbred lines, the team, with some new recruits, initiated a study using four sites across the native range of A. thaliana, from Oulu, Finland to Valencia, Spain, in which hundreds of natural ecotypes combined with a strategic array of mutants, were planted for continuous monitoring. Simultaneously with this ambitious field experiment, creative modelling efforts were being developed to predict the flowering time of specific genotypes under diverse climatic scenarios using agronomic models. This synthesis of genetics, ecology, agronomy and mathematical modelling was unique, and it provided unique insight into the genetic basis of adaptation. Their synthetic approach revealed, for example, that even well-known flowering time genes are expected to exhibit (and did exhibit) effects on flowering time only under certain ecological circumstances and life history backgrounds (Wilczek et al. 2009; Chew et al. 2012). The feat of bringing evolutionary ecologists (Cynthia Weinig, Tonia Korves, Amity Wilczek) in dialogue with population geneticists (Michael Purugganan), molecular geneticists (George Coupland, Rick Amasino, Caroline Dean) and modellers (Steve Welch), while engaging international collaborators (Outi Savolainen, Matthias Hoffmann) in fieldwork, was a Herculean accomplishment. A series of articles from this work was published in Science, PNAS, Molecular Ecology and a number of other prominent journals. Among the most notable findings of this programme were that A. thaliana shows evidence of climate adaptation, with geographic clines in adaptively significant life history traits as well as the loci associated with those traits (Caicedo et al. 2004; Stinchcombe et al. 2004, 2005; Korves et al. 2007; Wilczek et al. 2010). Moreover, genome-wide association studies revealed associations of loci with climate factors across the genome (Fournier-Level et al. 2011, 2013). Most recently (Wilczek et al. 2014), the team found evidence that climate change has caused banked seeds to no longer be optimally adapted to their locations of collection, but that that ecotypes from historically warmer locations performed better under current (warmer) conditions than banked seeds in their native location. As such, immigration of more warm-adapted genotypes into areas with climate change, not emergence from the seed bank or introduction of local banked seeds, is expected to be more effective at maintaining populations in the face of climate change. These empirical data, combined with predictive modelling, establishes a new standard for predictions of how organisms can respond to climate change. Annie was involved at the beginning stages of developing model genetic organisms into model ecological organisms. She helped shape the sorts of questions that could be addressed with this sort of collaboration and made ecological genetics a collaborative endeavour between ecologists, population geneticists and molecular geneticists. Always promoting collaboration over competition, she brokered many matches between PIs studying related phenomena and proposed opportunities to combine efforts in synergistic directions. The field has her to thank for the open and collaborative spirit she has infused it with. In addition to shaping the collaborative nature of the field of ecological genetics, Annie has been a valuable mentor to people at all stages. At Brown, she worked closely with her undergraduate students to involve them with every step of their research projects, from helping to design experiments to data collection, and analysis and presentation. Many of us are grateful for this effort, which has produced so many excellent students who have joined our laboratories as graduate students or technicians. It was here, too, that so many of her postdocs learned the craft of designing undergraduate projects that were self-contained, challenging and rewarding, providing a model for tapping the unique resources of undergraduates in research. Her numerous postdocs also benefitted from being members of such a cohesive laboratory, in which laboratory members could count on each other for technical help and conceptual exchange. Annie's generosity of time, creativity and opportunity were critical to the professional development of many of us. Personally, I will never take for granted the extreme generosity she extended to me when, after a very ill-timed postdoc in Yemen during what turned into its civil war, I found myself evacuated back stateside with no backup plan. I basically knocked on her door to ask for a short-term landing pad, and she opened it up in a manner I could never have expected. At that critical, awkward and very tricky time in my career, she welcomed me into her laboratory, involved me in the ongoing research and gave me new skills, intellectual companionship and a model for how to run a laboratory that was collegial, engaging and effective. I am certain that anyone who spent time in her laboratory benefitted in the same way, and several of her former postdocs (including Susan Dudley, Massimo Pigliucci, Cynthia Weinig, John Stinchcombe, Amity Wilczek and others) have expressed the same appreciation over the years. Annie has amassed several honours as a result of her creative contributions, including election to the National Academy of Sciences, the American Academy of Arts and Sciences, the American Association for the Advancement of Sciences and an Alexander von Humboldt Award, among others. She has been the President of the major professional societies in her field: the Society for the Study of Evolution and the American Society of Naturalists. While at Brown University, she was Stephen T. Olney Professor of Natural History, and she was also the director of the Environmental Change Initiative there, where she exercised her remarkable ability to communicate and synthesize across scientific subfields. UC Davis is now the beneficiary of Annie's energy and expertise, after she moved there in 2012. This Molecular Ecology Prize serves to honour her past accomplishments and inspire curiosity for what is to come.

BackgroundGenetic selection improves a population by increasing the frequency of favorable alleles. Understanding and monitoring allele frequency changes is, therefore, important to obtain more insight into the long-term effects of selection. This study aimed to investigate changes in allele frequencies and in results of genome-wide association studies (GWAS), and how those two are related to each other. This was studied in two maternal pig lines where selection was based on a broad selection index. Genotypes and phenotypes were available from 2015 to 2021.ResultsSeveral large changes in allele frequencies over the years were observed in both lines. The largest allele frequency changes were not larger than expected under drift based on gene dropping simulations, but the average allele frequency change was larger with selection. Moreover, several significant regions were found in the GWAS for the traits under selection, but those regions did not overlap with regions with larger allele frequency changes. No significant GWAS regions were found for the selection index in both lines, which included multiple traits, indicating that the index is affected by many loci of small effect. Additionally, many significant regions showed pleiotropic, and often antagonistic, associations with other traits under selection. This reduces the selection pressure on those regions, which can explain why those regions are still segregating, although the traits have been under selection for several generations. Across the years, only small changes in Manhattan plots were found, indicating that the genetic architecture was reasonably constant.ConclusionsNo significant GWAS regions were found for any of the traits under selection among the regions with the largest changes in allele frequency, and the correlation between significance level of marker associations and changes in allele frequency over one generation was close to zero for all traits. Moreover, the largest changes in allele frequency could be explained by drift and were not necessarily a result of selection. This is probably because selection acted on a broad index for which no significant GWAS regions were found. Our results show that selecting on a broad index spreads the selection pressure across the genome, thereby limiting allele frequency changes.

Coevolution is relentlessly creating and maintaining biodiversity and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e. coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g. between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian prey trait, such that predation is more intense with decreased phenotypic distance between their traits (phenotype matching). This antagonistic coevolution produces a complex pattern of bifurcations with bistability (initial state dependence) in a two-dimensional model for trait coevolution. Furthermore, with eco-evolutionary dynamics (so that the trait evolution affects predator-prey population dynamics), we find that coevolution can cause rich dynamics including anti-phase cycles, in-phase cycles, chaotic dynamics and deterministic predator extinction. Predator extinction is more likely to occur when the prey trait exhibits complete dominance rather than semidominance and when the predator trait evolves very rapidly. Our study illustrates how recognizing the genetic architectures of interacting ecological traits can be essential for understanding the population and evolutionary dynamics of coevolving species.

(Re)appreciating the role of life history in eco‐evolutionary dynamics

Recent studies have reported that population dynamics and evolutionary dynamics, occurring at different time scales, can be affected by each other (Fussmann et al., 2007). Accepting not only that ecology affects evolution but also that evolution affects ecology lead to our recognition of the existence of eco-evolutionary (ecogenetic) feedbacks. We have been exploring the interaction between population and evolutionary dynamics using an Artificial Life approach based on a 3D physically simulated environment in the context of the predator-prey and morphology-behavior coevolution. The first purpose of this study is to demonstrate coevolution between the predator and prey, and between morphology and behavior at the same time, by extending our previous model in which we evolved prey only to clarify the basic dynamics of eco-evolutionary feedbacks (Ito et al., ress). The second purpose is to explore the possibility of the extended model. We focus on the extinction, one of the most serious environmental issues, caused by the predatorprey and morphology-behavior coevolution. Specifically, we identify and elaborate a trait measure that can foresee an extinction of a species. We use Morphid Academy as an open-source simulation system to evolve virtual creatures in a 3D physically simulated environment, in which the morphologies and behaviors of virtual creatures are evolved using a genetic algorithm. To represent the interaction between the group of predators and prey, we simulate every individual of both population pools in a shared environment (Ito et al., ress). Both species’ traits and population size evolve concurrently. Each individual is evaluated on basis of 1) predation or escape ability and 2) cost of the body (it is incurred in proportion to an individual’s body volume). They have chance to reproduce, with the expected number of offspring being determined by the sum of the evaluation value of the parents. We observed the various patterns of extinction and looked for an index which changes when the ecosystem approaches extinction. We defined extinction as a sudden stop of the periodic increase and decrease in both population sizes (akin to the Lotka-Volterra system), followed by a state of very low (high) predator (prey) population size (near minimum (maximum) population size). We found a reasonable correlation between the remaining time until extinction and a simple index. The index we identified is the rate of change in the volume of body. For all cases of extinction observed in our experiments, first, we measured this index between two generations which are some generations apart from each other before extinction. Then we calculated the relation between this index of both species and the time from the generation to extinction. Analyzing 48 extinction events among 50 evolutionary experiments, we found that the remaining time until extinction was short when this index was large, and the time was long when this index was small. Specifically, calculating 95% confidence intervals, the shortest average remaining time until extinction was 151.7±13.8 generations when the increase rate was +0.15 to +0.20, and the longest was 452.7±12.3 generations when the rate was 0.60 to 0.65. We hypothesize that the reason for this correlation is as follows: In our previous study, we found that the large body volume contributes to the good predation / escape strategies because the agent which has the large volume of their body can move faster and can guard from contact by predator to the specific body parts using other body parts. Both species acquire good strategies with high body volume cost as a result of the arms race between predation and escape abilities. However, a predator with a good strategy and a large body can offset the cost of its large body with its high predation success when the population density is high, but not when the population density is low. This causes an increase in the extinction risk of the predators when the population is in the low-density phase of its Lotka-Volterra dynamic. It shows that trait evolution can affect extinction risk in the predatorprey relationship in eco-evolutionary feedbacks. We reported on the first step for searching the indices used for the prediction of extinction based on eco-evolutionary dynamics. The next focus must be whether or how broadly the simple index we found can be applied to the extinction events in nature.

Recent studies have reported that population dynamics and evolutionary dynamics, occurring at different time scales, can be affected by each other (Fussmann et al., 2007). Accepting not only that ecology affects evolution but also that evolution affects ecology lead to our recognition of the existence of eco-evolutionary (ecogenetic) feedbacks. We have been exploring the interaction between population and evolutionary dynamics using an Artificial Life approach based on a 3D physically simulated environment in the context of the predator-prey and morphology-behavior coevolution. The first purpose of this study is to demonstrate coevolution between the predator and prey, and between morphology and behavior at the same time, by extending our previous model in which we evolved prey only to clarify the basic dynamics of eco-evolutionary feedbacks (Ito et al., ress). The second purpose is to explore the possibility of the extended model. We focus on the extinction, one of the most serious environmental issues, caused by the predatorprey and morphology-behavior coevolution. Specifically, we identify and elaborate a trait measure that can foresee an extinction of a species. We use Morphid Academy as an open-source simulation system to evolve virtual creatures in a 3D physically simulated environment, in which the morphologies and behaviors of virtual creatures are evolved using a genetic algorithm. To represent the interaction between the group of predators and prey, we simulate every individual of both population pools in a shared environment (Ito et al., ress). Both species’ traits and population size evolve concurrently. Each individual is evaluated on basis of 1) predation or escape ability and 2) cost of the body (it is incurred in proportion to an individual’s body volume). They have chance to reproduce, with the expected number of offspring being determined by the sum of the evaluation value of the parents. We observed the various patterns of extinction and looked for an index which changes when the ecosystem approaches extinction. We defined extinction as a sudden stop of the periodic increase and decrease in both population sizes (akin to the Lotka-Volterra system), followed by a state of very low (high) predator (prey) population size (near minimum (maximum) population size). We found a reasonable correlation between the remaining time until extinction and a simple index. The index we identified is the rate of change in the volume of body. For all cases of extinction observed in our experiments, first, we measured this index between two generations which are some generations apart from each other before extinction. Then we calculated the relation between this index of both species and the time from the generation to extinction. Analyzing 48 extinction events among 50 evolutionary experiments, we found that the remaining time until extinction was short when this index was large, and the time was long when this index was small. Specifically, calculating 95% confidence intervals, the shortest average remaining time until extinction was 151.7±13.8 generations when the increase rate was +0.15 to +0.20, and the longest was 452.7±12.3 generations when the rate was 0.60 to 0.65. We hypothesize that the reason for this correlation is as follows: In our previous study, we found that the large body volume contributes to the good predation / escape strategies because the agent which has the large volume of their body can move faster and can guard from contact by predator to the specific body parts using other body parts. Both species acquire good strategies with high body volume cost as a result of the arms race between predation and escape abilities. However, a predator with a good strategy and a large body can offset the cost of its large body with its high predation success when the population density is high, but not when the population density is low. This causes an increase in the extinction risk of the predators when the population is in the low-density phase of its Lotka-Volterra dynamic. It shows that trait evolution can affect extinction risk in the predatorprey relationship in eco-evolutionary feedbacks. We reported on the first step for searching the indices used for the prediction of extinction based on eco-evolutionary dynamics. The next focus must be whether or how broadly the simple index we found can be applied to the extinction events in nature.

Complex traits are constructed during development by an intricate array of factors, and interactions among factors, including DNA, RNA, proteins, developmental modules, and various aspects of the biotic and abiotic environment. As a result, it is development that structures the relationship between the genotype and phenotype and thereby determines genetic architecture. The genotype–phenotype relationship plays a central role in phenotypic evolution because it determines how selection at the level of the phenotype is translated into evolutionary change at the level of the genotype (see ref. 1). Theoretical approaches to understanding phenotypic evolution have primarily focused either at the molecular genetic level, modeling evolution as changes in allele frequencies (the “population genetics” tradition), or at the gross phenotypic level, modeling evolution as changes in mean trait values by using the statistical relationship between molecular genetic variation and patterns of phenotypic variation (the “quantitative genetics” tradition). Both of these traditions have successfully advanced our understanding of phenotypic evolution, but both approaches are also inherently limited because they require an assumption of a relatively simple genotype–phenotype relationship. Thus, they are limited in their ability to incorporate the emerging data on the intricate patterns of genetic and developmental interactions that underlie the often remarkably complex genotype–phenotype relationship (2). For complex traits, this generally means that factors influencing trait expression, such as the intricate patterns of gene interactions or genotype-by-environment interactions, are either assumed absent or are included in a greatly simplified form (1). The Rice model embraces the complexity of genetics and development, rather than avoiding complexity by invoking simplifying assumptions. Although empirical research on the genetic architecture and developmental basis of trait expression has advanced at an extraordinary rate, formal links between the wealth of data that has been emerging and the process of phenotypic evolution have lagged behind. More recently, theoretical approaches …