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

Quantitative systems-based prediction of antimicrobial resistance evolution

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

Towards evolutionary predictions: Current promises and challenges

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Laplace's demon in biology: Models of evolutionary prediction

Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability.

Limited predictability of body length in a fish population

Recent theoretical studies have identified chaotic dynamics in eco-evolutionary models. Yet, empirical evidence for eco-evolutionary chaos in natural ecosystems is lacking. In this study, we combine analyses of empirical data and an eco-evolutionary model to uncover chaotic dynamics of body length in a fish population (northeast Arctic cod: Gadus morhua). Consistent with chaotic attractors, the largest Lyapunov exponent (LE) of empirical data is positive, and approximately matches the LE of the model calculation, thus suggesting the potential for chaotic dynamics in this fish population. We also find that the autocorrelation function (ACF) of both empirical data and eco-evolutionary model shows a similar lag of approximately 7 years. Our combined analyses of natural time series and mathematical models suggest that chaotic dynamics of a phenotypic trait may be driven by trait evolution. This finding supports a growing theory that eco-evolutionary feedbacks can produce chaotic dynamics.

Frequency dependence and the predictability of evolution in a changing environment

Frequency‐dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency‐dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and –2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi‐cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < –2, can produce self‐sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment‐dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution.

New Avenues for Old Travellers: Phenotypic Evolutionary Trends Meet Morphodynamics, and Both Enter the Global Change Biology Era

Evolutionary trends (ETs) are traditionally defined as substantial changes in the state of traits through time produced by a persistent condition of directional evolution. ETs might also include directional responses to ecological, climatic or biological gradients and represent the primary evolutionary pattern at high taxonomic levels and over long-time scales. The absence of a well-supported operative definition of ETs blurred the definition of conceptual differences between ETs and other key concepts in evolution such as convergence, parallel evolution, and divergence. Also, it prevented the formulation of modern guidelines for studying ETs and evolutionary dynamics related to them. In phenotypic evolution, the theory of morphodynamics states that the interplay between evolutionary factors such as phylogeny, evo-devo constraints, environment, and biological function determines morphological evolution. After introducing a new operative definition, here we provide a morphodynamics-based framework for studying phenotypic ETs, discussing how understanding the impact of these factors on ETs improves the explanation of links between biological patterns and processes underpinning directional evolution. We envisage that adopting a quantitative, pattern-based, and multifactorial approach will pave the way to new potential applications for this field of evolutionary biology. In this framework, by exploiting the catalysing effect of climate change on evolution, research on ETs induced by global change might represent an ideal arena for validating hypotheses about the predictability of evolution.

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

Being able to predict evolution under natural selection is important for many applied fields of biology, ranging from agriculture to medicine or conservation. However, this endeavor is complicated by factors that inherently limit our ability to predict the future, such as random fluctuations in the environment. Population genetic theory indicates that probabilistic predictions can still be made in this context, but the extent to which this holds empirically, and whether these predictions can be based on simple measurements, are still open questions. Making progress on answering these questions can be achieved by capitalizing on experiments where the environment is precisely controlled over many generations. Here, we used a pipetting robot to generate random time series of salinities with controlled patterns of fluctuations, which we imposed on a microalga, Dunaliella salina. Tracking the frequencies of two genotypes in a mixture by sequencing two short barcode sequences, we were able to show how patterns of fluctuating selection relate to the fluctuating environment. Interestingly, parts of these responses, but not all, could be predicted by simpler measurements in constant environments, allowing precise characterization the limits and prospects for predicting evolution in fluctuating environments.

Evolution: Predictability and the promise of ancient DNA

Is evolution predictable? Genomes from thousands-of-years-old stickleback suggest that, despite substantial stochasticity in the course of evolution, our understanding of the recent evolutionary past of this species was generally valid.

Eco-evolutionary dynamics may show an irreversible regime shift, illustrated by salmonids facing climate change

The enhanced or reduced growth of juvenile masu salmon (Oncorhynchus masou masou) may result from climate changes to their environment and thus impact on the eco-evolutionary dynamics of their life-history choices. Male juveniles with status, i.e., if their body size is larger than a threshold, stay in the stream and become resident males reproducing for multiple years, while those with smaller status, i.e., their body size is below the threshold, migrate to the ocean and return to the stream one year later to reproduce only once. Since juvenile growth is suppressed by the density of resident males, the fraction of resident males may stay in equilibrium or fluctuate wildly over a 2-year period. When the threshold value evolves, the convergence stable strategy may generate either an equilibrium or large fluctuations of male residents. If environmental changes occur faster than the rate of evolutionary adaptation, the eco-evolutionary dynamics exhibit a qualitative shift in the population dynamics. We also investigated the relative assessment models, in which individual life-history choices are made based on the individual’s relative status within the juvenile population. The eco-evolutionary dynamics are very different from the absolute assessment model, demonstrating the importance of understanding the mechanisms of life history choices when predicting the impacts of climate change.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Understanding childbirth as a complex salutogenic phenomenon: The EU COST BIRTH Action Special Collection

In 2014, the EU funded a four-year European Cooperation in Science and Technology (COST) Action to address the topic of childbirth. The COST Birth Action was a cross-European network, that brought together over 120 scientists, practitioners, activists and policy makers from 34 countries to work on intrapartum care. The central aim was to advance the state of research and practice in a specific area of great clinical and social importance, intrapartum care. The Action used inter and trans-disciplinary approaches to address birth from multiple perspectives and drew on complexity theory and the concept of salutogenesis (wellbeing). This special collection presents six papers produced from the Action and gives a sense of the range and depth of the work conducted. The Collection illustrates the knowledge that can be generated when a diverse group of people come together with a similar goals and perspectives.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.