Co-evolution Mechanism Research Articles

Torch: Strategy evolution in swarm robots using heterogeneous–homogeneous coevolution method

Because swarm robots have been applied widely in various fields, the evolution capability of their strategy have become of primary interest; therefore, the evolution method of swarm robots’ strategy has attracted attention in both industry and academia, especially for complex applications owing to their varied task scenes. Large amounts of researches have been conducted to realize strategy evolution in swarm robotics systems. However, there are few studies on the strategy evolution sufficiently examining the simultaneous improvement of evolutionary and strategy performance, which are two key demands of swarm robots. Besides, the strategy that evolved under the global information is difficult to fully adapt to the distributed task scenarios. To address these issues, this study presents a heterogeneous–homogeneous swarm coevolution method known as TORCH to improve the evolution capability of swarm robots. The method uses a swarm coevolution mechanism to accelerate the evolution. For the first time, we employ a behaviour expression tree in TORCH which expands the strategy search space of the evolved strategies. TORCH makes the swarm robots’ strategies evolve under local information conditions; hence, the evolutionary strategies are more adaptable to the distributed task scenarios. Extensive experiments have been conducted to verify the proposed TORCH, including a comparison with three methods based on the homogeneous swarm evolution method and parameter expression. The results demonstrate the superiority of the TORCH in terms of evolutionary efficiency improvement and strategy performance enhancement.

Understanding the co-evolutionary molecular mechanisms of resistance in the HIV-1 Gag and protease

Due to high human immunodeficiency virus type 1 (HIV-1) subtype C infections coupled with increasing antiretroviral treatment failure, the elucidation of complex drug resistance mutational patterns arising through protein co-evolution is required. Despite the inclusion of potent protease inhibitors Lopinavir (LPV) and Darunavir (DRV) in second- and third-line therapies, many patients still fail treatment due to the accumulation of mutations in protease (PR) and recently, Gag. To understand the co-evolutionary molecular mechanisms of resistance in the HIV-1 PR and Gag, we performed 100 ns molecular dynamic simulations on multidrug resistant PR’s when bound to LPV, DRV or a mutated A431V NC|p1 Gag cleavage site (CS). Here we showed that distinct changes in PR’s active site, flap and elbow regions due to several PR resistance mutations (L10F, M46I, I54V, L76V, V82A) were found to alter LPV and DRV drug binding. However, binding was significantly exacerbated when the mutant PRs were bound to the NC|p1 Gag CS. Although A431V was shown to coordinate several residues in PR, the L76V PR mutation was found to have a significant role in substrate recognition. Consequently, a greater binding affinity was observed when the mutated substrate was bound to an L76V-inclusive PR mutant (Gbind: −62.46 ± 5.75 kcal/mol) than without (Gbind: −50.34 ± 6.28 kcal/mol). These data showed that the co-selection of resistance mutations in the enzyme and substrate can simultaneously constrict regions in PR’s active site whilst flexing the flaps to allow flexible movement of the substrate and multiple, complex mechanisms of resistance to occur. Communicated by Ramaswamy H. Sarma

Large-scale characterization of sex pheromone communication systems in Drosophila

Insects use sex pheromones as a reproductive isolating mechanism to attract conspecifics and repel heterospecifics. Despite the profound knowledge of sex pheromones, little is known about the coevolutionary mechanisms and constraints on their production and detection. Using whole-genome sequences to infer the kinship among 99 drosophilids, we investigate how phylogenetic and chemical traits have interacted at a wide evolutionary timescale. Through a series of chemical syntheses and electrophysiological recordings, we identify 52 sex-specific compounds, many of which are detected via olfaction. Behavioral analyses reveal that many of the 43 male-specific compounds are transferred to the female during copulation and mediate female receptivity and/or male courtship inhibition. Measurement of phylogenetic signals demonstrates that sex pheromones and their cognate olfactory channels evolve rapidly and independently over evolutionary time to guarantee efficient intra- and inter-specific communication systems. Our results show how sexual isolation barriers between species can be reinforced by species-specific olfactory signals.

Investigation of Salmonella Phage-Bacteria Infection Profiles: Network Structure Reveals a Gradient of Target-Range from Generalist to Specialist Phage Clones in Nested Subsets.

Bacteriophages that lyse Salmonella enterica are potential tools to target and control Salmonella infections. Investigating the host range of Salmonella phages is a key to understand their impact on bacterial ecology, coevolution and inform their use in intervention strategies. Virus–host infection networks have been used to characterize the “predator–prey” interactions between phages and bacteria and provide insights into host range and specificity. Here, we characterize the target-range and infection profiles of 13 Salmonella phage clones against a diverse set of 141 Salmonella strains. The environmental source and taxonomy contributed to the observed infection profiles, and genetically proximal phages shared similar infection profiles. Using in vitro infection data, we analyzed the structure of the Salmonella phage–bacteria infection network. The network has a non-random nested organization and weak modularity suggesting a gradient of target-range from generalist to specialist species with nested subsets, which are also observed within and across the different phage infection profile groups. Our results have implications for our understanding of the coevolutionary mechanisms shaping the ecological interactions between Salmonella phages and their bacterial hosts and can inform strategies for targeting Salmonella enterica with specific phage preparations.

Trust-based partner switching among partitioned regions promotes cooperation in public goods game.

In this paper, the coevolution mechanism of trust-based partner switching among partitioned regions on an adaptive network is studied. We investigate a low-information approach to building trust and cooperation in public goods games. Unlike reputation, trust scores are only given to players by those with whom they have a relationship in the game, depending on the game they play together. A player’s trust score for a certain neighbor is given and known by that player only. Players can adjust their connections to neighbors with low trust scores by switching their partners to other players. When switching partners, players divide other nodes in the network into three regions: immediate neighbors as the known region, indirectly connected second-order neighbors as the intermediate region, and other nodes as the unknown region. Such choices and compartmentalization often occur in global and regional economies. Our results show that preference for switching to partners in the intermediate region is not conducive to spreading cooperation, while random selection has the disadvantage of protecting the cooperator. However, selecting new partners in the remaining two regions based on the average trust score of the known region performs well in both protecting partners and finding potential cooperators. Meanwhile, by analyzing the parameters, we find that the influence of vigilance increasing against unsatisfactory behavior on evolution direction depends on the level of cooperation reward.

A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization

Under the current volatile business environment, the requirement of flexible production is becoming increasingly urgent. As an innovative production mode, seru-system with reconfigurability can overcome the lack of flexibility in traditional flow lines. Compared with pure seru-system, the hybrid seru-system composed of both serus and production lines is more practical for adapting to many production processes. This paper addresses a specific hybrid seru-system scheduling optimization problem (HSSOP), which includes three strongly coupled sub-problems, i.e., hybrid seru formation, seru scheduling and flow line scheduling. To minimize the makespan of the whole hybrid seru-system, we propose an efficient cooperative coevolution algorithm (CCA). To tackle three sub-problems, specific sub-algorithms are designed based on the characteristic of each sub-problem, i.e., a sub-space exploitation algorithm for hybrid seru formation, an estimation of distribution algorithm for seru scheduling, and a first-arrive-first-process heuristic for flow line scheduling. Since three sub-problems are coupled, a cooperation coevolution mechanism is proposed for the integrated algorithm by information sharing. Moreover, a batch reassign rule is designed to overcome the mismatch of partial solutions during cooperative coevolution. To enhance the exploitation ability, problem-specific local search methods are designed and embedded in the CCA. In addition to the investigation about the effect of parameter setting, extensive computational tests and comparisons are carried out which demonstrate the effectiveness and efficiency of the CCA in solving the HSSOP.

English

Secreted fungal effector proteins and their host targets are good examples to understand the mechanism of host-pathogen co-evolution with genes involved in the interaction undergoing positive selection. SIX genes (secreted in xylem) are obtained via horizontal transfer and can be found within the formae speciales of Fusarium oxysporum. SIX6 and SIX9 of F. oxysporum f. spp. cubense (Foc) are predicted to play a role as effectors. However, their involvement in the pathogenicity of Foc in banana plants has not been determined yet. In the susceptible banana cultivar, we found that the SIX6 and SIX9 genes of Foc TR4 were highly expressed in roots, but not in corms or leaves. The host, however, expressed the pathogenesis-related (PR) genes, PR-1 and PR-3, in corms earlier than in the roots. Phylogenetic analysis on SIX6 and SIX9 genes of F. oxysporum has revealed the separation of SIX6 and SIX9 of Foc from other formae speciales. This leads to detecting genes under positive selection using the ratio nonsynonymous to synonymous substitution rates (Ka/Ks). SIX6 of Foc showed an increase in diversity, but insufficient to drive positive selection. Conversely, SIX9 of Foc showed no divergence in the dN/dS ratio distribution, indicating purifying selection. © 2021 Friends Science Publishers

Diverse evidence for grasslands since the Eocene in Patagonia

Timing and ecological steps of the rise and expansion of grasslands differ not only geographically but also according to the type of available information. Patagonia (Argentina) was largely considered the region where grasslands and grazers early evolved in the Eocene, mostly based on functional morphology of mammals. However, recent paleobotanical studies question this traditional view and maintain that this occurred after early Miocene. To tackle this discrepancy, we conduct a multidisciplinary and integrative approach using diverse lines of evidence obtained from middle Eocene - middle Miocene tuffaceous deposits of central and northern Patagonia. Gathered data correspond to sedimentary facies, paleosols, insect trace fossils, opal phytoliths and fossil mammals (tooth morphology, body size). Long-lasting and frequent volcanic ashfalls caused permanent disturbances in physical scenarios, biotic systems and grassy habitats; just like herbivory of mammals through coevolutionary grass-grazer mechanisms. The results indicate that soils, vegetation, insects and mammal herbivores begun synchronously to record diverse traits related to grasslands showing mosaic vegetation since middle Eocene (~44 Ma). These traits include mollic paleosols, abundant and persistent dung beetle trace fossils (Coprinisphaera ichnofacies), phytolith assemblages attributable to savannas and medium to large-size hypsodont mammals. We also track the evolutionary history after the reduction of closed forests, recognizing alternating subhumid savannas with riparian forests, where the relative frequency between grasses, palms, trees and shrubs fluctuated in time and space; and desert vegetation (shrublands) dominated by palms and shrubs during dryer periods. The advent and expansion of grasslands can be linked to the cooling and drying period subsequent to the Early Eocene Climate Optimum.

Cycad-Weevil Pollination Symbiosis Is Characterized by Rapidly Evolving and Highly Specific Plant-Insect Chemical Communication.

Coevolution between plants and insects is thought to be responsible for generating biodiversity. Extensive research has focused largely on antagonistic herbivorous relationships, but mutualistic pollination systems also likely contribute to diversification. Here we describe an example of chemically-mediated mutualistic species interactions affecting trait evolution and lineage diversification. We show that volatile compounds produced by closely related species of Zamia cycads are more strikingly different from each other than are other phenotypic characters, and that two distantly related pollinating weevil species have specialized responses only to volatiles from their specific host Zamia species. Plant transcriptomes show that approximately a fifth of genes related to volatile production are evolving under positive selection, but we find no differences in the relative proportion of genes under positive selection in different categories. The importance of phenotypic divergence coupled with chemical communication for the maintenance of this obligate mutualism highlights chemical signaling as a key mechanism of coevolution between cycads and their weevil pollinators.

Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization.

Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results show that AGLDPSO performs better than or at least comparable with some other state-of-the-art large-scale optimization algorithms, even the winner of the competition on large-scale optimization, on all the 35 benchmark functions from both IEEE Congress on Evolutionary Computation (IEEE CEC2010) and IEEE CEC2013 large-scale optimization test suites.

Cooperation in Social Dilemmas: A Group Game Model with Double-Layer Networks

The combination of complex networks and game theory is one of the most suitable ways to describe the evolutionary laws of various complex systems. In order to explore the evolution of group cooperation in multiple social dilemmas, a model of a group game with a double-layer network is proposed here. Firstly, to simulate a multiplayer game under multiple identities, we combine a double-layer network and public goods game. Secondly, in order to make an individual’s strategy selection process more in line with a practical context, a new strategy learning method that incorporates individual attributes is designed here, referred to as a “public goods game with selection preferences” (PGG-SP), which makes strategic choices that are more humane and diversified. Finally, a co-evolution mechanism for strategies and topologies is introduced based on the double-layer network, which effectively explains the dynamic game process in real life. To verify the role of multiple double-layer networks with a PGG-SP, four types of double-layer networks are applied in this paper. In addition, the corresponding game results are compared between single-layer, double-layer, static, and dynamic networks. Accordingly, the results show that double-layer networks can facilitate cooperation in group games.

The Rhinolophus affinis bat ACE2 and multiple animal orthologs are functional receptors for bat coronavirus RaTG13 and SARS-CoV-2

Bat coronavirus (CoV) RaTG13 shares the highest genome sequence identity with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among all known coronaviruses, and also uses human angiotensin converting enzyme 2 (hACE2) for virus entry. Thus, SARS-CoV-2 is thought to have originated from bat. However, whether SARS-CoV-2 emerged from bats directly or through an intermediate host remains elusive. Here, we found that Rhinolophus affinis bat ACE2 (RaACE2) is an entry receptor for both SARS-CoV-2 and RaTG13, although the binding of RaACE2 to the receptor-binding domain (RBD) of SARS-CoV-2 is markedly weaker than that of hACE2. We further evaluated the receptor activities of ACE2s from additional 16 diverse animal species for RaTG13, SARS-CoV, and SARS-CoV-2 in terms of S protein binding, membrane fusion, and pseudovirus entry. We found that the RaTG13 spike (S) protein is significantly less fusogenic than SARS-CoV and SARS-CoV-2, and seven out of sixteen different ACE2s function as entry receptors for all three viruses, indicating that all three viruses might have broad host rages. Of note, RaTG13 S pseudovirions can use mouse, but not pangolin ACE2, for virus entry, whereas SARS-CoV-2 S pseudovirions can use pangolin, but not mouse, ACE2 enter cells efficiently. Mutagenesis analysis revealed that residues 484 and 498 in RaTG13 and SARS-CoV-2 S proteins play critical roles in recognition of mouse and human ACE2s. Finally, two polymorphous Rhinolophous sinicus bat ACE2s showed different susceptibilities to virus entry by RaTG13 and SARS-CoV-2 S pseudovirions, suggesting possible coevolution. Our results offer better understanding of the mechanism of coronavirus entry, host range, and virus-host coevolution.

Viruses in Extreme Environments, Current Overview, and Biotechnological Potential.

Virus research has advanced significantly since the discovery of the tobacco mosaic virus (TMV), the characterization of its infection mechanisms and the factors that determine their pathogenicity. However, most viral research has focused on pathogenic viruses to humans, animals and plants, which represent only a small fraction in the virosphere. As a result, the role of most viral genes, and the mechanisms of coevolution between mutualistic viruses, their host and their environment, beyond pathogenicity, remain poorly understood. This review focuses on general aspects of viruses that interact with extremophile organisms, characteristics and examples of mechanisms of adaptation. Finally, this review provides an overview on how knowledge of extremophile viruses sheds light on the application of new tools of relevant use in modern molecular biology, discussing their value in a biotechnological context.

Driving force of industrial technology innovation: coevolution of multistage overseas M&A integration and knowledge network reconfiguration

PurposeThe purpose of the paper is to examine the coevolutionary dynamics between multistage overseas mergers and acquisitions (M&A) integration and knowledge network reconfiguration and the impact of this coevolution on industrial technology innovation.Design/methodology/approachThis paper builds a coevolution analysis framework in stages and constructs structural equation models for empirical tests using the Chinese technology-sourcing overseas M&A events that occurred from 2001 to 2012.FindingsOverseas M&A integration and knowledge network reconfiguration are in a coevolutionary relationship, driving industrial technology innovation. The acquirer adopts initial integration degree that matches the resource relatedness between the acquiring and acquired parties, promoting initial industrial technology innovation through initial knowledge network reconfiguration. Initial knowledge network reconfiguration will feed back to the M&A integration decision in the mid-to-late stage through increasing knowledge similarity and narrowing network position difference. The higher the improvement of mid-to-late integration degree, the more it can drive mid-to-late industrial technology innovation through mid-to-late knowledge network reconfiguration.Research limitations/implicationsFuture research can accurately classify overseas M&A integration stages through case tracking and explore other network attributes.Practical implicationsPractical guidelines are provided for managers on how to implement a multistage overseas M&A integration strategy, optimize knowledge network reconfiguration and promote industrial technology innovation. Significant practical implications are presented, especially in academia, society and quality of life.Originality/valueDifferent from the previous research considering M&A integration as a single-stage decision, this paper emphasizes the dynamics of the M&A integration process and explores the coevolution mechanism of multistage overseas M&A integration and knowledge network reconfiguration.

Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution.

The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. For the purpose, a grouping mechanism and a co-evolution mechanism are designed and integrated into the framework for ameliorating the convergence and the diversity of multi-objective optimization solutions and keeping the exploration and exploitation of swarm intelligence algorithm in balance. The grouping mechanism is employed to improve the diversity of search agents for increasing coverage of search space. The co-evolution mechanism is used to improve the convergence to the true Pareto optimal front by the interaction of search agents. Quantitative and qualitative outcomes prove that the framework prominently ameliorate the convergence accuracy and convergence speed of MOGOA. The performance of the presented algorithm has been benchmarked by several standard test functions, such as CEC2009, ZDT and DTLZ. The diversity and convergence of the obtained multi-objective optimization solutions are quantitatively and qualitatively compared with the original MOGOA by using two performance indicators (GD and IGD). The results on test suits show that the diversity and convergence of the obtained solutions are significantly improved. On several test functions, some statistical indicators are more than doubled. The validity of the results has been verified by the Wilcoxon rank-sum test.

HPV sketch of its lifestyle

Human papillomaviruses (HPV) are small icosahedral DNA non-enveloped viruses, adapted to human beings from the very beginning, since the apparition of the first humanoids. They are part of the large papillomaviruses group, which co-evolved with the first reptile species, around 350 million years ago. These viruses persist for years and even decades together with their animal or human hosts, in epithelial cells and/or mucosae, being considered rather commensal or producing chronic unapparent “infections” in most cases. There are described more than 150 HPV species, included in Alpha, Beta, Gamma, and some in Miu and Niu genera. The mechanisms of co-evolution between the human host and HPV are complex and depend on the host immune tolerance (with unknown advantages from HPV, to be tolerated) and HPV replication, with the promotion of host epithelial cell persistence, which in the case of HPV species with ma­lig­nant risk could cause an “immortality” of the host-cell and consequently malignancy. The mucosal-associated HPV lesions, condyloma acuminatum and focal epithelial hy­per­plasia, are benign lesions caused by types of HPV with mucosal affinities. Other lesions are malignant, such as cervical or other anogenital cancers (of vulva, vagina, pe­nis and anus) and head and neck cancers. The HPV spe­cies associated with cancers are included mostly in Alpha HPV genus. Cutaneous associated HPV lesions – common warts, plantar warts, flat warts, filiform warts, pigmented warts, epidermoid cysts – are benign lesions associated with cutaneous HPV strains, but Bowen disease is a type of skin cancer associated with alpha HPV. There are also sug­ges­tions that some of non-melanoma skin cancers could be as­sociated with some beta HPVs. Curative measures in­clude to­pi­cal cytotoxic and antiviral agents, topical im­mu­no­mo­du­la­tory agents, intralesional immunotherapy, destructive me­thods and surgery. Vaccination is the only way to de­crease the incidence of HPV-induced cancers and should be in­di­ca­ted for both boys and girls before natural infection oc­curs.

Digital Twin Bionics: A Biological Evolution-Based Digital Twin Approach for Rapid Product Development

With intensified market competition, product development procedure is accelerated, requiring rapid product innovation and efficient collaboration between design and manufacturing. However, there still exist information islands, prohibiting integration of product life cycle processes. To address this issue, bionics and digital twin (DT) are combined as a potential solution. The concepts, framework, and features of digital twin bionics (DTB) is originally proposed, with co-evolution mechanism of product-twins (including virtual and physical products) and production-twins (including virtual and physical production) elaborated. A symbiotic co-evolution mechanism is presented to integrate the processes of product development and manufacturing. In detail, the supporting technologies, such as industrial internet of things, cloud edge computing, big data, and artificial intelligence, as well as the potential key technologies, are illustrated. A case study for rapid development of automotive body-in-white in an industrial robotics welding production line is investigated to verify the applicability of the proposed framework. The results suggest that the integration of bionics and DTs can accelerate the innovations and developments of new products and management the process of production construction.

The research of co-evolution mechanisms between cross-border e-commerce and manufacturing cluster: An Agent-based model

China’s cross-border e-commerce industry presents the development trend as the service industry clusters, and interacts closely with the traditional manufacturing clusters. However, the mechanism for the coordinated evolution of the two kinds of clusters is not clear yet. This paper defines the cross-border e-commerce and the manufacturing cluster under collaboration as a complex socio-economic adaptation system that includes manufacturers, service providers, government behaviors and market demands. An agent-based collaborative model is designed by three types of agents and four driving interaction mechanisms, then the co-evolution mechanisms are observed and discussed in the process of dynamic evolution. Based on the rationality and feasibility of the model, the research explored the trend of co-evolution through scenario simulations and analysis, improved the mechanisms of co-evolution, and pointed out that cross-border e-commerce is one of the ways to transform and upgrade the manufacturing cluster. The conclusion of the study provides evolutionary ideas for export-oriented industrial clusters to coordinate with the cross-border e-commerce.

The Coevolution Mechanism of Stakeholder Strategies in the Recycled Resources Industry Innovation Ecosystem: The View of Evolutionary Game Theory

The Coevolution Mechanism of Stakeholder Strategies in the Recycled Resources Industry Innovation Ecosystem: The View of Evolutionary Game Theory

Coevolutionary Dynamics of Organic-Inorganic Interactions, Hydrocarbon Generation, and Shale Gas Reservoir Preservation: A Case Study from the Upper Ordovician Wufeng and Lower Silurian Longmaxi Formations, Fuling Shale Gas Field, Eastern Sichuan Basin

Shale gas deposits are self-sourced, self-accumulating, and self-preserving in the Upper Ordovician Wufeng Formation and Lower Silurian Longmaxi Formation of the Fuling Shale Gas Field in the eastern Sichuan Basin. They were both seemingly mixed by secondary oil cracking and kerogen cracking gases during the high maturation window. The reservoir space primarily consists of mineral pores and organic matter (OM) pores, and the shale gas was mainly trapped by a high-pressure system. In this study, the Fuling O3w-S1l Shale Gas Field in the eastern Sichuan Basin was used as a case study to discuss the coevolutionary process and organic-inorganic interactions of hydrocarbon generation, accumulation, and preservation. The results indicate that the processes and mechanisms of organic-inorganic interactions and coevolution of hydrocarbon generation and reservoir preservation are quite different among the shale graptolite zones (GZ) with respect to hydrocarbon generation, types and characteristics of shale gas reservoirs, seal characteristics, and their spatiotemporal relations. In the WF2-LM4 GZ, the favorable OM, biogenic authigenic quartz and organic-inorganic interactions are highly coupled, leading to the high level of coevolution demonstrated within the field, as well as to the favorable conditions for shale gas accumulation. Conversely, the overlying LM5-LM8 GZ seemingly exhibits early densification and late charge and has a reverse mode of reservoir development (i.e., low degree of coevolution). These two coevolutionary processes were conducive to the development of a high degree of spatiotemporal matching between the reservoir (i.e., WF2-LM4 GZ) and the seal (i.e., LM5-LM8 GZ). This is due to underlying differences in their coevolutionary histories. The synthetic work presented here on the coevolutionary processes and mechanisms of formation for organic-inorganic interactions and hydrocarbon generation and reservoir preservation reveals insights into the driving mechanisms of shale gas enrichment, providing a basis for effectively predicting favorable enrichment intervals for shale gas worldwide.