Coevolutionary Dynamics Research Articles

Disequilibrium and complexity across scales: a patch-dynamics framework for organizational ecology

Based on equilibrium assumptions, traditional ecological models have been widely applied in the fields of management and organization studies. While research using these models is still ongoing, studies have nonetheless struggled with ways to address multiple levels of analysis, uncertainty, and complexity in their analyses. This paper conceptualizes the dynamic co-evolution mechanisms that operate in an ecosystem across multiple organizational scales. Specifically, informed by recent advances in modelling in biology, a general ‘patch-dynamics’ framework that is theoretically and methodologically able to capture disequilibrium, uncertainty, disturbances, and changes in organizational populations or ecosystems, as complex and dynamically evolving resource environments are introduced. Simulation models are built to show the patch-dynamics framework’s functioning and test its robustness. The patch-dynamics framework and modelling methodology integrates equilibrium and disequilibrium perspectives, co-evolutions across multiple organization levels, uncertainties, and random disturbances into a single framework, opening new avenues for future research on topics in the field of management and organization studies, as well as on the mechanisms that shape ecosystems. Such a framework has the potential to help analyse the sustainability and healthiness of the business environment, and deserves more attention in future research on management and organization theory, particularly in the context of significant uncertainty and disturbances in business and management practice. Overall, the paper offers a distinct theoretical perspective and methodology for modelling population and ecosystem dynamics across different scales.

A dynamic dual-population co-evolution multi-objective evolutionary algorithm for constrained multi-objective optimization problems

Many multi-objective evolutionary algorithms are proposed to handle constrained multi-objective optimization problems. Nevertheless, they often fail to appropriately balance feasibility, convergence and diversity of the population. This paper proposed a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to solve this issue. In DDCMEA, a dynamic dual-population co-evolution strategy is employed to balance the convergence and the feasibility by dynamically adjusting the offspring number of the two populations. In the early stage of evolution, the algorithm mainly focuses on the convergence and more offspring of the first population are generated. In the late stage of evolution, the algorithm mainly focuses on the feasibility and more offspring of the second population are generated. Finally, feasible solutions with good convergence could be obtained. To further enhance the diversity of the offspring and obtain feasible solutions with a wide spread of distribution, the evolution operators of the genetic algorithm and the differential evolution are chosen as the search engines for the first population and the second population, respectively. The performance of DDCMEA is further tested through thirty-one bench-mark test problems and two real-world problems in comparison with other five state-of-the-art algorithms. The results show the proposed algorithm DDCMEA achieves competitive performance when handling constrained multi-objective optimization problems.

Human-Gut Phages Harbor Sporulation Genes.

Spore-forming bacteria are prevalent in mammalian guts and have implications for host health and nutrition. The production of dormant spores is thought to play an important role in the colonization, persistence, and transmission of these bacteria. Spore formation also modifies interactions among microorganisms such as infection by phages. Recent studies suggest that phages may counter dormancy-mediated defense through the expression of phage-carried sporulation genes during infection, which can alter the transitions between active and inactive states. By mining genomes and gut-derived metagenomes, we identified sporulation genes that are preferentially carried by phages that infect spore-forming bacteria. These included genes involved in chromosome partitioning, DNA damage repair, and cell wall-associated functions. In addition, phages contained homologs of sporulation-specific transcription factors, notably spo0A, the master regulator of sporulation, which could allow phages to control the complex genetic network responsible for spore development. Our findings suggest that phages could influence the formation of bacterial spores with implications for the health of the human gut microbiome, as well as bacterial communities in other environments. IMPORTANCE Phages acquire bacterial genes and use them to alter host metabolism in ways that enhance phage fitness. To date, most auxiliary genes replace or modulate enzymes that are used by the host for nutrition or energy production. However, phage fitness is affected by all aspects of host physiology, including decisions that reduce the metabolic activity of the cell. Here, we focus on endosporulation, a complex and ancient form of dormancy found among the Bacillota that involves hundreds of genes. By coupling homology searches with host classification, we identified 31 phage-carried homologs of sporulation genes that are mostly limited to phages infecting spore-forming bacteria. Nearly one-third of the homologs recovered were regulatory genes, suggesting that phages may manipulate host genetic networks by tapping into their control elements. Our findings also suggest a mechanism by which phages can overcome the defensive strategy of dormancy, which may be involved in coevolutionary dynamics of spore-forming bacteria.

Compensatory relationship between low-complexity regions and gene paralogy in the evolution of prokaryotes

The evolution of genomes in all life forms involves two distinct, dynamic types of genomic changes: gene duplication (and loss) that shape families of paralogous genes and extension (and contraction) of low-complexity regions (LCR), which occurs through dynamics of short repeats in protein-coding genes. Although the roles of each of these types of events in genome evolution have been studied, their co-evolutionary dynamics is not thoroughly understood. Here, by analyzing a wide range of genomes from diverse bacteria and archaea, we show that LCR and paralogy represent two distinct routes of evolution that are inversely correlated. The emergence of LCR is a prominent evolutionary mechanism in fast evolving, young protein families, whereas paralogy dominates the comparatively slow evolution of old protein families. The analysis of multiple prokaryotic genomes shows that the formation of LCR is likely a widespread, transient evolutionary mechanism that temporally and locally affects also ancestral functions, but apparently, fades away with time, under mutational and selective pressures, yielding to gene paralogy. We propose that compensatory relationships between short-term and longer-term evolutionary mechanisms are universal in the evolution of life.

Demographic fluctuations and selection during host-parasite co-evolution interactively increase genetic diversity.

Host-parasite interactions can cause strong demographic fluctuations accompanied by selective sweeps of resistance/infectivity alleles. Both demographic bottlenecks and frequent sweeps are expected to reduce the amount of segregating genetic variation and therefore might constrain adaptation during co-evolution. Recent studies, however, suggest that the interaction of demographic and selective processes is a key component of co-evolutionary dynamics and may rather positively affect levels of genetic diversity available for adaptation. Here, we provide direct experimental testing of this hypothesis by disentangling the effects of demography, selection and their interaction in an experimental host-parasite system. We grew 12 populations of a unicellular, asexually reproducing algae (Chlorella variabilis) that experienced either growth followed by constant population sizes (three populations), demographic fluctuations (three populations), selection induced by exposure to a virus (three populations), or demographic fluctuations together with virus-induced selection (three populations). After 50 days (~50 generations), we conducted whole-genome sequencing of each algal host population. We observed more genetic diversity in populations that jointly experienced selection and demographic fluctuations than in populations where these processes were experimentally separated. In addition, in those three populations that jointly experienced selection and demographic fluctuations, experimentally measured diversity exceeds expected values of diversity that account for the cultures' population sizes. Our results suggest that eco-evolutionary feedbacks can positively affect genetic diversity and provide the necessary empirical measures to guide further improvements of theoretical models of adaptation during host-parasite co-evolution.

Host and parasite functional morphology jointly explain parasite specificity

Abstract Host–parasite coevolution is a major diversifying force. However, while the genetic determinants of host–parasite coevolution have received substantial attention, it remains unresolved how morphological adaptations contribute to host–parasite coevolutionary dynamics. We used a well‐studied and ecologically important host–parasite system to explore morphological adaptation in host–parasite interactions. In this daphniid‐fungal parasite system, infection occurs when fungal spores puncture the host gut and enter the body cavity. Prior work found genetic differences in the parasite associated with spore size. We studied how host gut traits, parasite spore size and host immune responses influenced the infection process. We collected parasite spores from two host species, the larger Daphnia dentifera and the smaller Ceriodaphnia dubia, and exposed both host species to spores sourced from each host. The ability of a spore to embed in the host gut and to penetrate into the body cavity was influenced by the host species that was exposed to the parasite (‘exposure host species’) and the species from which the spores were sourced (‘source host species’). Spores sourced from D. dentifera were better able to attack both hosts, but were especially good at attacking D. dentifera. These differences likely resulted from morphological differences, with a striking correspondence between the diameter of host guts and the size of the parasite spores. Immune responses were influenced by both exposure and source host, with D. dentifera‐sourced spores triggering a larger immune response in D. dentifera than in C. dubia. In addition, in C. dubia exposure hosts, D. dentifera‐sourced spores triggered a greater immune response than did C. dubia‐sourced spores. Only 13.5% of hosts that had at least one parasite spore penetrate ended up with terminal infections; all but one of these infections occurred in D. dentifera hosts exposed to D. dentifera‐sourced spores. Overall, infection was influenced by morphological traits of both hosts and parasites, with the outcome at each step of the infection process—and the likelihood of terminal infection—being determined by both the exposure host and the source host. Read the free Plain Language Summary for this article on the Journal blog.

Metagenomic evidence for antibiotics-driven co-evolution of microbial community, resistome and mobilome in hospital sewage

Overconsumption of antibiotics is an immediate cause for the emergence of antimicrobial resistance (AMR) and antibiotic resistant bacteria (ARB), though its environmental impact remains inadequately clarified. There is an urgent need to dissect the complex links underpinning the dynamic co-evolution of ARB and their resistome and mobilome in hospital sewage. Metagenomic and bioinformatic methods were employed to analyze the microbial community, resistome and mobilome in hospital sewage, in relation to data on clinical antibiotic use collected from a tertiary-care hospital. In this study, resistome (1,568 antibiotic resistance genes, ARGs, corresponding to 29 antibiotic types/subtypes) and mobilome (247 types of mobile genetic elements, MGEs) were identified. Networks connecting co-occurring ARGs with MGEs encompass 176 nodes and 578 edges, in which over 19 types of ARGs had significant correlations with MGEs. Prescribed dosage and time-dependent antibiotic consumption were associated with the abundance and distributions of ARGs, and conjugative transfer of ARGs via MGEs. Variation partitioning analyses show that effects of conjugative transfer were most likely the main contributors to transient propagation and persistence of AMR. We have presented the first evidence supporting idea that use of clinical antibiotics is a potent driving force for the development of co-evolving resistome and mobilome, which in turn supports the growth and evolution of ARB in hospital sewage. The use of clinical antibiotics calls for greater attention in antibiotic stewardship and management.

Network adaption based on environment feedback promotes cooperation in co-evolutionary games

In evolutionary games, the choices of individuals form the cooperation environments, and the environments in turn affect the behaviors of individuals. Such environment feedback can be utilized to facilitate the evolution of cooperation. On the other hand, adaptive social structures have been thought to promote cooperation, in which the individuals can switch their interacting neighbors. Here, we propose a co-evolutionary game model, in which individuals can adjust connections based on environment feedback during the evolution. In particular, the individuals determine whether to adjust their connections by comparing the local cooperation environment with the global one. Meanwhile, we use the parameter ω to adjust the time scale between the strategy evolution and the network adaptation in the co-evolutionary dynamics. Our results show that, the network adaption based on the environment feedback can significantly promote cooperation. We find that, relatively fast network adaptation (large ω) can better facilitate the evolution of cooperation. We further investigate how the rationality of individuals in network adaptation process affects the cooperation, and find that a high level of rationality is beneficial to improve cooperation. Through monitoring the local cooperation environments of the individuals, the fractions of the reconnecting individuals and the average degrees of cooperators and defectors, respectively, we provide some intuitive explanations for the promotion of cooperation by the co-evolutionary scheme. Our results may provide some references on the study about how to improve cooperation by adapting the social structures based on environment feedback in the real world.

Coevolutionary Dynamics of Host Immune and Parasite Virulence Based on an Age-Structured Epidemic Model

Hosts can activate a defensive response to clear the parasite once being infected. To explore how host survival and fecundity are affected by host-parasite coevolution for chronic parasitic diseases, in this paper, we proposed an age-structured epidemic model with infection age, in which the parasite transmission rate and parasite-induced mortality rate are structured by the infection age. By use of critical function analysis method, we obtained the existence of the host immune evolutionary singular strategy which is a continuous singular strategy (CSS). Assume that parasite-induced mortality begins at infection age [Formula: see text] and is constant v thereafter. We got that the value of the CSS, [Formula: see text], monotonically decreases with respect to infection age [Formula: see text] (see Case (I)), while it is non-monotone if the constant v positively depends on the immune trait c (see Case (II)). This non-monotonicity is verified by numerical simulations and implies that the direction of immune evolution depends on the initial value of immune trait. Besides that, we adopted two special forms of the parasite transmission rate to study the parasite's virulence evolution, by maximizing the basic reproduction ratio [Formula: see text]. The values of the convergence stable parasite's virulence evolutionary singular strategies [Formula: see text] and [Formula: see text] increase monotonically with respect to time lag L (i.e., the time lag between the onset of transmission and mortality). At the singular strategy [Formula: see text] and [Formula: see text], we further obtained the expressions of the case mortalities [Formula: see text] and how they are affected by the time lag L. Finally, we only presented some preliminary results about host and parasite coevolution dynamics, including a general condition under which the coevolutionary singular strategy [Formula: see text] is evolutionarily stable.

Discovery of a rapidly evolving yeast defense factor, KTD1, against the secreted killer toxin K28

Secreted protein toxins are widely used weapons in conflicts between organisms. Elucidating how organisms genetically adapt to defend themselves against these toxins is fundamental to understanding the coevolutionary dynamics of competing organisms. Within yeast communities, "killer" toxins are secreted to kill nearby sensitive yeast, providing a fitness advantage in competitive growth environments. Natural yeast isolates vary in their sensitivity to these toxins, but to date, no polymorphic genetic factors contributing to defense have been identified. We investigated the variation in resistance to the killer toxin K28 across diverse natural isolates of the Saccharomyces cerevisiae population. Using large-scale linkage mapping, we discovered a novel defense factor, which we named KTD1. We identified many KTD1 alleles, which provided different levels of K28 resistance. KTD1 is a member of the DUP240 gene family of unknown function, which is rapidly evolving in a region spanning its two encoded transmembrane helices. We found that this domain is critical to KTD1's protective ability. Our findings implicate KTD1 as a key polymorphic factor in the defense against K28 toxin.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic. As an important part of this field, intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers. In particular, it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks. For this purpose, we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks. First, individual interactions are described by a modified activity-driven network (ADN) model. Then, we establish a novel node-based susceptible–infected–recovered–susceptible (SIRS) model to characterize the information diffusion dynamics. On these bases, three synergetic intervention strategies are formulated. Second, we derive the critical threshold of the controlled-SIRS system via stability analysis. Accordingly, we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget. Third, we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense, in which the optimal intervention inputs are obtained through optimal control theory and a forward–backward sweep algorithm. Finally, extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Infectious disease ecology and evolution in a changing world.

Infectious disease ecology and evolution in a changing world.

Forest health in the Anthropocene: the emergence of a novel tree disease is associated with poplar cultivation.

Plant domestication and movement are large contributors to the success of new diseases. The introduction of new host species can result in accelerated evolutionary changes in pathogens, affecting long-established coevolutionary dynamics. This has been observed in poplars where severe epidemics of pathogens that were innocuous in their natural pathosystems occurred following host domestication. The North American fungus Sphaerulina musiva is responsible for endemic leaf spots on Populus deltoides. We show that the expansion of poplar cultivation resulted in the emergence of a new lineage of this pathogen that causes stem infections on a new host, P. balsamifera. This suggests a host shift since this is not a known host. Genome analysis of this emerging lineage reveals a mosaic pattern with islands of diversity separated by fixed genome regions, which is consistent with a homoploid hybridization event between two individuals that produced a hybrid swarm. Genome regions of extreme divergence and low diversity are enriched in genes involved in host-pathogen interactions. The specialization of this emerging lineage to a new host and its clonal propagation represents a serious threat to poplars and could affect both natural and planted forests. This work provides a clear example of the changes created by the intensification of tree cultivation that facilitate the emergence of specialized pathogens, jeopardizing the natural equilibrium between hosts and pathogens. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Tracing the Evolution of SMBHs and Stellar Objects in Galaxy Mergers: A Multi-mass Direct N-body Model

By using direct N-body numerical simulations, we model the dynamical coevolution of two supermassive black holes (SMBHs) and the surrounding stars in merging galaxies. In order to investigate how different stellar components evolve during the merger, we generate evolved stellar distributions with an initial mass function. Special schemes have also been developed to deal with some rare but interesting events, such as the tidal disruption of main-sequence stars, plunging low-mass stars, white dwarfs, neutron stars and stellar-mass black holes, and the partial tidal disruption of red giants or asymptotic giant branch stars. Our results indicate that the formation of a bound SMBH binary will enhance the capture rates of stellar objects by SMBHs. Compared to the equal stellar mass model, the multi-mass model tends to result in a higher average mass of disrupted stars. Instead of being tidally disrupted by the SMBH, roughly half of the captured main-sequence stars will directly plunge into the SMBH because of their small stellar radii. Giant stars, on the other hand, can be stripped of their envelopes if they are close enough to the SMBH. Though most remnants of the giant stars can survive after the disruption, a small fraction still could plunge into the SMBH quickly or after many orbital periods. Our results also indicate significant mass segregation of compact stars at the beginning of the merger, and then this effect is destroyed as the two SMBHs form a bound binary.

Coevolution of epidemic and infodemic on higher-order networks

Gathering events, e.g., going to gyms and meetings, are ubiquitous and crucial in the spreading phenomena, which induce higher-order interactions, and thus can be described as higher-order networks. Previous studies on the coevolution of epidemic-infodemic dynamics ignored the higher-order interactions in the social system, which affects our understanding of the reality spreading. We propose a mathematical framework for the coevolution of epidemic and infodemic on higher-order networks described by simplicial complex, and introduce the Microscopic Markov Chain Approach (MMCA) and mean-field approach to establish the dynamic process. We study the coevolution mathematical model on both artificial simplicial complex and real-world higher-order networks and find that the higher-order interactions show a ’double-edged sword’ role in shaping epidemic size, which is dependent on the breakout of infodemic. Furthermore, the higher-order networks enrich the phase diagram, inducing the emergence of discontinuous phase transition, hysteresis loop region, double transition and inter-epidemic region.

Disentangling the complex gene interaction networks between rice and the blast fungus identifies a new pathogen effector.

Studies focused solely on single organisms can fail to identify the networks underlying host-pathogen gene-for-gene interactions. Here, we integrate genetic analyses of rice (Oryza sativa, host) and rice blast fungus (Magnaporthe oryzae, pathogen) and uncover a new pathogen recognition specificity of the rice nucleotide-binding domain and leucine-rich repeat protein (NLR) immune receptor Pik, which mediates resistance to M. oryzae expressing the avirulence effector gene AVR-Pik. Rice Piks-1, encoded by an allele of Pik-1, recognizes a previously unidentified effector encoded by the M. oryzae avirulence gene AVR-Mgk1, which is found on a mini-chromosome. AVR-Mgk1 has no sequence similarity to known AVR-Pik effectors and is prone to deletion from the mini-chromosome mediated by repeated Inago2 retrotransposon sequences. AVR-Mgk1 is detected by Piks-1 and by other Pik-1 alleles known to recognize AVR-Pik effectors; recognition is mediated by AVR-Mgk1 binding to the integrated heavy metal-associated (HMA) domain of Piks-1 and other Pik-1 alleles. Our findings highlight how complex gene-for-gene interaction networks can be disentangled by applying forward genetics approaches simultaneously to the host and pathogen. We demonstrate dynamic coevolution between an NLR integrated domain and multiple families of effector proteins.

Discrete and continuum models for the coevolutionary dynamics between CD8+ cytotoxic T lymphocytes and tumour cells.

We present an individual-based model for the coevolutionary dynamics between CD8+ cytotoxic T lymphocytes (CTLs) and tumour cells. In this model, every cell is viewed as an individual agent whose phenotypic state is modelled by a discrete variable. For tumour cells, this variable represents a parameterization of the antigen expression profiles, while for CTLs it represents a parameterization of the target antigens of T-cell receptors (TCRs). We formally derive the deterministic continuum limit of this individual-based model, which comprises a non-local partial differential equation for the phenotype distribution of tumour cells coupled with an integro-differential equation for the phenotype distribution of CTLs. The biologically relevant homogeneous steady-state solutions of the continuum model equations are found. The linear-stability analysis of these steady-state solutions is then carried out in order to identify possible conditions on the model parameters that may lead to different outcomes of immune competition and to the emergence of patterns of phenotypic coevolution between tumour cells and CTLs. We report on computational results of the individual-based model, and show that there is a good agreement between them and analytical and numerical results of the continuum model. These results shed light on the way in which different parameters affect the coevolutionary dynamics between tumour cells and CTLs. Moreover, they support the idea that TCR-tumour antigen binding affinity may be a good intervention target for immunotherapy and offer a theoretical basis for the development of anti-cancer therapy aiming at engineering TCRs so as to shape their affinity for cancer targets.

Coevolution of trust dynamics and formal contracting in governing inter-organisation exchange

Coevolution of trust dynamics and formal contracting in governing inter-organisation exchange

Coevolution of Trader Networks and Follower Dynamics in Social Trading

Coevolution of Trader Networks and Follower Dynamics in Social Trading

Recurrence-Plot Visualization and Quantitative Analysis of Long-Term Co-Evolutionary Dynamics in a Simulated Financial Market with ZIP Traders

Recurrence-Plot Visualization and Quantitative Analysis of Long-Term Co-Evolutionary Dynamics in a Simulated Financial Market with ZIP Traders