Eco-Evolutionary Feedback and the Tuning of Proto-Developmental Life Cycles

Multicellular organisms depend on developmental programs to coordinate growth and differentiation from single cells, but the origins of development are unclear. A possible starting point is stochastic phenotypic variation generated by molecular noise. Given appropriate environmental conditions, noise-driven differentiation could conceivably evolve so as to come under regulatory control; however, abiotic conditions are likely to be restrictive. Drawing from an experimental system, we present a model in which environmental fluctuations are coupled to population growth. We show that this coupling generates stable selection for a single optimal strategy that is largely insensitive to environmental conditions, including the number of competitors, carrying capacity of the environment, difference in growth rates among phenotypic variants, and population density. We argue that this optimal strategy establishes stabilizing conditions likely to improve the quality and reliability of information experienced by evolving organisms, thus increasing opportunity for the evolutionary emergence of developmental programs.     

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.     

The evolutionary impacts of conservation actions

Conservation management for environmental sustainability is now ubiquitous. The ecological effects of these actions are well-intentioned and well-known. Although conservation biologists and managers increasingly incorporate evolutionary considerations into management plans, the evolutionary consequences of management strategies have remained relatively unexplored and unconsidered. But what are the evolutionary consequences? Here, we advocate a new research agenda focused on identifying, predicting, and countering the evolutionary consequences of conservation management. We showcase the examples of park creation and invasive species management, and speculate further on five other major methods of management. Park creation may cause selection for altered dispersal and behavior that utilizes human foods and structures. Management of invasive species may favor the evolution of resistance to or tolerance of control methods. In these and other cases, evolution may cause deviations from the predicted consequences of management strategies optimized without considering evolution, particularly when management results in or coincides with major environmental change, if population size change strongly, or if life histories are short enough to allow more rapid evolution. We call for research focused on: (1) experimental predictions and tests of evolution under particular management strategies, (2) widespread monitoring of managed populations and communities, and (3) meta-analysis and theoretical study aimed at simplifying the process of evolutionary prediction, particularly at systematizing a means of identifying traits likely to evolve due to likely existing genetic variance or high mutation rates. Ultimately, conservation biologists should incorporate evolutionary prediction into management planning to prevent the evolutionary domestication of the species that they are trying to protect.     

The impact of bottlenecks on microbial survival,adaptation, and phenotypic switching in host–pathogen interactions

Microbial pathogens and viruses can often maintain sufficient population diversity to evade a wide range of host immune responses. However, when populations experience bottlenecks, as occurs frequently during initiation of new infections, pathogens require specialized mechanisms to regenerate diversity. We address the evolution of such mechanisms, known as stochastic phenotype switches, which are prevalent in pathogenic bacteria. We analyze a model of pathogen diversification in a changing host environment that accounts for selective bottlenecks, wherein different phenotypes have distinct transmission probabilities between hosts. We show that under stringent bottlenecks, such that only one phenotype can initiate new infections, there exists a threshold stochastic switching rate below which all pathogen lineages go extinct, and above which survival is a near certainty. We determine how quickly stochastic switching rates can evolve by computing a fitness landscape for the evolutionary dynamics of switching rates, and analyzing its dependence on both the stringency of bottlenecks and the duration of within‐host growth periods. We show that increasing the stringency of bottlenecks or decreasing the period of growth results in faster adaptation of switching rates. Our model provides strong theoretical evidence that bottlenecks play a critical role in accelerating the evolutionary dynamics of pathogens.     

Hysteresis Can Grant Fitness in Stochastically Varying Environment

Although the existence of multiple stable phenotypes of living organisms enables random switching between phenotypes as well as non-random history dependent switching called hysteresis, only random switching has been considered in prior experimental and theoretical models of adaptation to variable environments. This work considers the possibility that hysteresis may also evolve together with random phenotype switching to maximize population growth. In addition to allowing the possibility that switching rates between different phenotypes may depend not only on a continuous environmental input variable, but also on the phenotype itself, the present work considers an opportunity cost of the switching events. This opportunity cost arises as a result of a lag phase experimentally observed after phenotype switching and stochastic behavior of the environmental input. It is shown that stochastic environmental variation results in maximal asymptotic growth rate when organisms display hysteresis for sufficiently slowly varying environmental input. At the same time, sinusoidal input does not cause evolution of memory suggesting that the connection between the lag phase, stochastic environmental variation and evolution of hysteresis is a result of a stochastic resonance type phenomenon.     

The evolution of resistance through costly acquired immunity

We examine the evolutionary dynamics of resistance to parasites through acquired immunity. Resistance can be achieved through the innate mechanisms of avoidance of infection and reduced pathogenicity once infected, through recovery from infection and through remaining immune to infection: acquired immunity. We assume that each of these mechanisms is costly to the host and find that the evolutionary dynamics of innate immunity in hosts that also have acquired immunity are quantitatively the same as in hosts that possess only innate immunity. However, compared with resistance through avoidance or recovery, there is less likely to be polymorphism in the length of acquired immunity within populations. Long-lived organisms that can recover at intermediate rates faced with fast-transmitting pathogens that cause intermediate pathogenicity (mortality of infected individuals) are most likely to evolve long-lived acquired immunity. Our work emphasizes that because whether or not acquired immunity is beneficial depends on the characteristics of the disease, organisms may be selected to only develop acquired immunity to some of the diseases that they encounter.     

Courting disaster: How diversification rate affects fitness under risk

Life is full of risk. To deal with this uncertainty, many organisms have evolved bet-hedging strategies that spread risk through phenotypic diversification. These rates of diversification can vary by orders of magnitude in different species. Here we examine how key characteristics of risk and organismal ecology affect the fitness consequences of variation in diversification rate. We find that rapid diversification is strongly favored when the risk faced has a wide spatial extent, with a single disaster affecting a large fraction of the population. This advantage is especially great in small populations subject to frequent disaster. In contrast, when risk is correlated through time, slow diversification is favored because it allows adaptive tracking of disasters that tend to occur in series. Naturally evolved diversification mechanisms in diverse organisms facing a broad array of environmental risks largely support these results. The theory presented in this article provides a testable ecological hypothesis to explain the prevalence of slow stochastic switching among microbes and rapid, within-clutch diversification strategies among plants and animals.     

Chaos and the (un)predictability of evolution in a changing environment

Among the factors that may reduce the predictability of evolution, chaos, characterized by a strong dependence on initial conditions, has received much less attention than randomness due to genetic drift or environmental stochasticity. It was recently shown that chaos in phenotypic evolution arises commonly under frequency‐dependent selection caused by competitive interactions mediated by many traits. This result has been used to argue that chaos should often make evolutionary dynamics unpredictable. However, populations also evolve largely in response to external changing environments, and such environmental forcing is likely to influence the outcome of evolution in systems prone to chaos. We investigate how a changing environment causing oscillations of an optimal phenotype interacts with the internal dynamics of an eco‐evolutionary system that would be chaotic in a constant environment. We show that strong environmental forcing can improve the predictability of evolution by reducing the probability of chaos arising, and by dampening the magnitude of chaotic oscillations. In contrast, weak forcing can increase the probability of chaos, but it also causes evolutionary trajectories to track the environment more closely. Overall, our results indicate that, although chaos may occur in evolution, it does not necessarily undermine its predictability.     

Evolution of mate choice and the so‐called magic traits in ecological speciation

Non‐random mating provides multiple evolutionary benefits and can result in speciation. Biological organisms are characterised by a myriad of different traits, many of which can serve as mating cues. We consider multiple mechanisms of non‐random mating simultaneously within a unified modelling framework in an attempt to understand better which are more likely to evolve in natural populations going through the process of local adaptation and ecological speciation. We show that certain traits that are under direct natural selection are more likely to be co‐opted as mating cues, leading to the appearance of magic traits (i.e. phenotypic traits involved in both local adaptation and mating decisions). Multiple mechanisms of non‐random mating can interact so that trait co‐evolution enables the evolution of non‐random mating mechanisms that would not evolve alone. The presence of magic traits may suggest that ecological selection was acting during the origin of new species.     

Physiological mechanisms of thermoregulation in reptiles: a review

The thermal dependence of biochemical reaction rates means that many animals regulate their body temperature so that fluctuations in body temperature are small compared to environmental temperature fluctuations. Thermoregulation is a complex process that involves sensing of the environment, and subsequent processing of the environmental information. We suggest that the physiological mechanisms that facilitate thermoregulation transcend phylogenetic boundaries. Reptiles are primarily used as model organisms for ecological and evolutionary research and, unlike in mammals, the physiological basis of many aspects in thermoregulation remains obscure. Here, we review recent research on regulation of body temperature, thermoreception, body temperature set-points, and cardiovascular control of heating and cooling in reptiles. The aim of this review is to place physiological thermoregulation of reptiles in a wider phylogenetic context. Future research on reptilian thermoregulation should focus on the pathways that connect peripheral sensing to central processing which will ultimately lead to the thermoregulatory response.     

Development in context: the timely emergence of eco-devo

Ecological development or 'eco-devo' examines the mechanisms of developmental regulation in real-world environments, providing an integrated approach for investigating both plastic and canalized aspects of phenotypic expression. This synthetic discipline brings a current understanding of environmentally mediated regulatory systems to studies of genetic variation, ecological function and evolutionary change. Eco-devo is emerging at a critical point in time, as researchers try to understand and predict the future of organisms in a changing world. Precise knowledge of the external and internal environmental cues, signaling pathways and genetic elements implicated in developmental outcomes will provide key insights to the immediate tolerance and potential evolutionary resilience of organisms to the altered physical and biotic conditions created by human activities.     

Circadian clock-regulated physiological outputs: Dynamic responses in nature

The plant circadian clock is involved in the regulation of numerous processes. It serves as a timekeeper to ensure that the onset of key developmental events coincides with the appropriate conditions. Although internal oscillating clock mechanisms likely evolved in response to the earth's predictable day and night cycles, organisms must integrate a range of external and internal cues to adjust development and physiology. Here we introduce three different clock outputs to illustrate the complexity of clock control. Clock-regulated diurnal growth is altered by environmental stimuli. The complexity of the photoperiodic flowering pathway highlights numerous nodes through which plants may integrate information to modulate the timing of flowering. Comparative analyses among ecotypes that differ in flowering response reveal additional environmental cues and molecular processes that have developed to influence flowering. We also explore the process of cold acclimation, where circadian inputs, light quality, and stress responses converge to improve freezing tolerance in anticipation of colder temperatures.     

The Evolution of Resource Adaptation: How Generalist and Specialist Consumers Evolve

Why and how specialist and generalist strategies evolve are important questions in evolutionary ecology. In this paper, with the method of adaptive dynamics and evolutionary branching, we identify conditions that select for specialist and generalist strategies. Generally, generalist strategies evolve if there is a switching benefit; specialists evolve if there is a switching cost. If the switching cost is large, specialists always evolve. If the switching cost is small, even though the consumer will first evolve toward a generalist strategy, it will eventually branch into two specialists.     

Bistability in feedback circuits as a byproduct of evolution of evolvability

Noisy bistable dynamics in gene regulation can underlie stochastic switching and is demonstrated to be beneficial under fluctuating environments. It is not known, however, if fluctuating selection alone can result in bistable dynamics. Using a stochastic model of simple feedback networks, we apply fluctuating selection on gene expression and run in silico evolutionary simulations. We find that independent of the specific nature of the environment–fitness relationship, the main outcome of fluctuating selection is the evolution of increased evolvability in the network; system parameters evolve toward a nonlinear regime where phenotypic diversity is increased and small changes in genotype cause large changes in expression level. In the presence of noise, the evolution of increased nonlinearity results in the emergence and maintenance of bistability. Our results provide the first direct evidence that bistability and stochastic switching in a gene regulatory network can emerge as a mechanism to cope with fluctuating environments. They strongly suggest that such emergence occurs as a byproduct of evolution of evolvability and exploitation of noise by evolution.     

sOAR: a tool for modelling optimal animal life‐history strategies in cyclic environments

Periodic environments determine the life cycle of many animals across the globe and the timing of important life history events, such as reproduction and migration. These adaptive behavioural strategies are complex and can only be fully understood (and predicted) within the framework of natural selection in which species adopt evolutionary stable strategies. We present sOAR, a powerful and user‐friendly implementation of the well‐established framework of optimal annual routine modelling. It allows determining optimal animal life history strategies under cyclic environmental conditions using stochastic dynamic programming. It further includes the simulation of population dynamics under the optimal strategy. sOAR provides an important tool for theoretical studies on the behavioural and evolutionary ecology of animals. It is especially suited for studying bird migration. In particular, we integrated options to differentiate between costs of active and passive flight into the optimal annual routine modelling framework, as well as options to consider periodic wind conditions affecting flight energetics. We provide an illustrative example of sOAR where food supply in the wintering habitat of migratory birds significantly alters the optimal timing of migration. sOAR helps improving our understanding of how complex behaviours evolve and how behavioural decisions are constrained by internal and external factors experienced by the animal. Such knowledge is crucial for anticipating potential species’ response to global environmental change.     

Evolutionary tracking is determined by differential selection on demographic rates and density dependence

Recent ecological forecasts predict that ~25% of species worldwide will go extinct by 2050. However, these estimates are primarily based on environmental changes alone and fail to incorporate important biological mechanisms such as genetic adaptation via evolution. Thus, environmental change can affect population dynamics in ways that classical frameworks can neither describe nor predict. Furthermore, often due to a lack of data, forecasting models commonly describe changes in population demography by summarizing changes in fecundity and survival concurrently with the intrinsic growth rate (r). This has been shown to be an oversimplification as the environment may impose selective pressure on specific demographic rates (birth and death) rather than directly on r (the difference between the birth and death rates). This differential pressure may alter population response to density, in each demographic rate, further diluting the information combined to produce r. Thus, when we consider the potential for persistence via adaptive evolution, populations with the same r can have different abilities to persist amidst environmental change. Therefore, we cannot adequately forecast population response to climate change without accounting for demography and selection on density dependence. Using a continuous‐time Markov chain model to describe the stochastic dynamics of the logistic model of population growth and allow for trait evolution via mutations arising during birth events, we find persistence via evolutionary tracking more likely when environmental change alters birth rather than the death rate. Furthermore, species that evolve responses to changes in the strength of density dependence due to environmental change are less vulnerable to extinction than species that undergo selection independent of population density. By incorporating these key demographic considerations into our predictive models, we can better understand how species will respond to climate change.     

Genetic Architecture Promotes the Evolution and Maintenance of Cooperation

When cooperation has a direct cost and an indirect benefit, a selfish behavior is more likely to be selected for than an altruistic one. Kin and group selection do provide evolutionary explanations for the stability of cooperation in nature, but we still lack the full understanding of the genomic mechanisms that can prevent cheater invasion. In our study we used Aevol, an agent-based, in silico genomic platform to evolve populations of digital organisms that compete, reproduce, and cooperate by secreting a public good for tens of thousands of generations. We found that cooperating individuals may share a phenotype, defined as the amount of public good produced, but have very different abilities to resist cheater invasion. To understand the underlying genetic differences between cooperator types, we performed bio-inspired genomics analyses of our digital organisms by recording and comparing the locations of metabolic and secretion genes, as well as the relevant promoters and terminators. Association between metabolic and secretion genes (promoter sharing, overlap via frame shift or sense-antisense encoding) was characteristic for populations with robust cooperation and was more likely to evolve when secretion was costly. In mutational analysis experiments, we demonstrated the potential evolutionary consequences of the genetic association by performing a large number of mutations and measuring their phenotypic and fitness effects. The non-cooperating mutants arising from the individuals with genetic association were more likely to have metabolic deleterious mutations that eventually lead to selection eliminating such mutants from the population due to the accompanying fitness decrease. Effectively, cooperation evolved to be protected and robust to mutations through entangled genetic architecture. Our results confirm the importance of second-order selection on evolutionary outcomes, uncover an important genetic mechanism for the evolution and maintenance of cooperation, and suggest promising methods for preventing gene loss in synthetically engineered organisms.     

Modelling the evolution of genomes with integrated external and internal functions

The genomes that organisms transmit between generations contain information about different kinds of functions. The genome with the "best" mix and number of genes for these functions is the one that natural selection favours. Here I introduce a new way to model simple organisms with genes for external and internal functions, and use it to study the evolution of genome size. The external functions are exemplified by resource use and the internal functions by mutation control (repair). It is shown that even with a suitable proportion of genes for mutation control, the genomes in the organisms do not forever incorporate genes that increase resource use. Instead they evolve towards an optimal genome of limited size. The optimal proportion of genes for mutation control is shown to have an upper limit given by the ease with which transmission accuracy is improved by adding extra genes for this purpose to the genome. The model illustrates how natural selection on genomes integrates systems for the transmission of genetic information with systems relating to the external adaptation of the organism. It also opens up for other, more detailed theoretical investigations of genome functions.     

The evolutionary emergence of stochastic phenotype switching in bacteria

Stochastic phenotype switching - or bet hedging - is a pervasive feature of living systems and common in bacteria that experience fluctuating (unpredictable) environmental conditions. Under such conditions, the capacity to generate variable offspring spreads the risk of being maladapted in the present environment, against offspring likely to have some chance of survival in the future. While a rich subject for theoretical studies, little is known about the selective causes responsible for the evolutionary emergence of stochastic phenotype switching. Here we review recent work - both theoretical and experimental - that sheds light on ecological factors that favour switching types over non-switching types. Of particular relevance is an experiment that provided evidence for an adaptive origin of stochastic phenotype switching by subjecting bacterial populations to a selective regime that mimicked essential features of the host immune response. Central to the emergence of switching types was frequent imposition of 'exclusion rules' and 'population bottlenecks' - two complementary faces of frequency dependent selection. While features of the immune response, exclusion rules and bottlenecks are likely to operate in many natural environments. Together these factors define a set of selective conditions relevant to the evolution of stochastic switching, including antigenic variation and bacterial persistence.     

Resonance effects and outbreaks in ecological time series

Organismal response to environmental variability is an important aspect of ecological processes. We propose new mechanisms whereby environmental variability can cause cyclic population outbreaks due to the nonlinearity of the organismal response. We consider stage-structured populations that respond to variable environments with variable diapause or dormancy, and in which cyclic changes of the environment induce a resonance-like boost in the population size. If there is also a stochastic component of variation in the environment, the population outbreaks are magnified by the phenomenon of "stochastic resonance". The results show that large population fluctuations may not be due to extrinsic or intrinsic factors alone, but to a nonlinear interaction between the external environment and internal population processes. Indeed, in the presence of such nonlinearities even very small environmental fluctuations can cause massive fluctuations in population size. Our theoretical results may help to explain periodic population cycles and outbreak dynamics found in many infectious diseases and pest species. We also discuss the evolution of the response parameters that regulate diapause or dormancy and promote the outbreak dynamics in variable environments.