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Review

Adaptive Evolution of Marine Organisms: A Bibliometric Analysis Based on CiteSpace

by
Ning Mao
1,2,
Ye Zhu
3,
Junyue Wang
1,2,
Xin Shen
1,2,*,
Jun Hou
3 and
Lingzhan Miao
3,*
1
Jiangsu Key Laboratory of Marine Bioresources and Environment, Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222006, China
2
Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang 222006, China
3
Key Laboratory of Integrated Regulation and Resources Development on Shallow Lakes of Ministry of Education, Hohai University, Nanjing 210098, China
*
Authors to whom correspondence should be addressed.
Water 2023, 15(23), 4162; https://doi.org/10.3390/w15234162
Submission received: 7 November 2023 / Revised: 26 November 2023 / Accepted: 27 November 2023 / Published: 30 November 2023
(This article belongs to the Section Water Quality and Contamination)

Abstract

:
(1) Background: The increasing complexity of marine organism habitats and the impact of climate change on the marine environment have garnered global attention towards the adaptive evolution of marine organisms. However, there still remains a lack of understanding regarding the historical development and research trends of adaptive evolution in marine organisms. (2) Methods: We conducted an analysis on the adaptive evolution of marine organisms published in the Web of Science (WOS) over the past two decades. Through a visualization of publications, authors, institutional affiliations, and keywords based on CiteSpace, this review examines research contributions, identifies research hotspots, and outlines future development trends in this field. (3) Results: The findings indicate a surge in the advancement of the adaptive evolution of marine organisms, particularly since 2017, with tight collaboration among European nations. Additionally, Chinese research institutions and scholars have emerged as some of the most significant contributors in this field. (4) Conclusions: This review provides an overview of the main research hotspots and frontiers in the field pertaining to adaptive evolution of marine organisms, as well as the historical timeline of research and development. This review has offered scientific guidance to scholars and institutions concerned with conducting research on marine organisms and adaptive evolution.

1. Introduction

Adaptive evolution is a process that results in alterations to the phenotypic characteristics of a population, allowing for local adaptation within a given environment over an extended period of evolutionary time. When the genetic phenotype of organisms undergoes modifications in response to a specific environment, thereby improving the survival and reproductive success of the species within that environment, the resulting phenotype is considered to be adaptable [1]. This adaptive relationship between gene mutations and the environment reflects the adaptability of biological functional traits. The heritable variations observed among marine organisms can ultimately be attributed to evolutionary occurrences. However, the absence of suitable adaptive genetic variation has the potential to impact the population’s evolutionary pattern and rate [2]. Additionally, the systematic distribution and hereditary characteristics of marine organisms may be influenced by factors such as depth, sea water pressure, food availability, trench and ridge morphology, larval development duration, and other pertinent variables [3,4,5]. Consequently, studying the adaptive evolutionary traits of marine organisms within a specific ecological setting is instrumental in elucidating the mechanisms by which these organisms adapt to their environment through natural selection. Comprehending and investigating the evolutionary lineage of the origin and the adaptability in marine species assumes paramount significance.
Due to their sessile or benthic nature and limited mobility, marine organisms are highly sensitive to the alterations in environmental conditions such as seawater composition and temperature. The advent of high-throughput sequencing technology has facilitated the investigation of marine organisms at the molecular level, which aids in the analysis and prediction of gene expression patterns in marine organisms as they adapt to their surroundings [6]. Numerous studies on the adaptability of marine organisms to environment have been conducted by scholars across the world, leading to its emergence as a popular research area. Several scholars have conducted research on the evolutionary relationship of marine organisms across different sea depths, documented the evolutionary diversity of these species [7], and employed specific genetic variation in nucleotide positions in the mitochondrial genome to monitor the adaptation of local populations [8,9]. Additionally, researchers have investigated sexual differences and explored the mechanisms of environmental adaptation in marine organisms, particularly in high salinity and high temperature conditions [10,11,12,13,14,15]. Additional research have expanded upon the diverse adaptations exhibited by related species inhabiting deep seas and shallow seas, encompassing their characteristics and molecular adaptation mechanisms [16,17,18]. Nevertheless, contemporary scholars predominantly focused on specific topics within this domain, thereby lacking a comprehensive understanding of the prominent research areas, cutting-edge advancements, developmental trajectories, and prevailing challenges within this field. This review work seeks to answer the following questions: What are the primary potential trends for the advancement of research on the adaptive evolution of marine organisms? What are the prominent research areas and hotspots? In order to investigate the focal areas and emerging patterns in the study of adaptive evolution among marine organisms, it is imperative to meticulously categorize the existing scholarly literature in this domain, perform bibliometric and knowledge graph analyses, and elucidate the quantitative characteristics and developmental trend of the research literature.
Therefore, this review employed a bibliometric analysis and knowledge mapping analysis, with the core collection database of Web of Science (WOS) serving as the primary data source, from which works in the literature pertaining to the adaptive evolution of marine organisms were selected as samples, by utilizing clustering knowledge mapping based on keyword co-occurrence and mutation detection in the CiteSpace 6.2.R4 visualization software. This review aims to objectively uncover the research hotspots and development trends in the field of adaptive evolution of marine organisms, thereby shedding light on its research foundation.

2. Materials and Methods

2.1. Data Source

Since WOS currently serves as the primary English-language database compatible with CiteSpace, it was employed to perform a search for the keywords [“marine organism” or “ocean organism” or “ocean species” or “marine species” and “adaptive evolution”] on 24 October 2023. The retrieved publications from the WOS core collection encompass the period from 2003 to 2023 and specifically focus on the topic of adaptive evolution in marine organisms. To ensure the relevance of the gathered information, only research papers and reviews were included, while any research directions unrelated to the adaptive evolution of marine organisms were manually excluded. A total of 659 articles were screened and subsequently exported in plain text format, comprising complete records and cited references. Citespecies 6.2.R4 software was used to conduct a bibliometric analysis on the adaptive evolution of marine organisms research.

2.2. Data Anlaysis

This review has employed bibliometric techniques to quantify the pertinent documents, authors, institutions, countries, journal distribution, keyword co-occurrences, references, and other data, and conducted a thorough analysis. The bibliometric tool, CiteSpace, was utilized for visualization purposes, leveraging its knowledge graph analysis approach to examine author collaboration, national collaboration networks, research progress, and areas of focus, while visually presenting the statistical outcomes of the academic literature [19]. To ascertain the prominent areas of research within the domain of adaptive evolution of marine organisms and the interconnections among countries where the relevant literature has been published, we constructed a keyword co-occurrence network and a transnational cooperation network map. This was achieved by employing keyword clustering and time trend analysis, along with the visualization of national cooperation networks (Figure 1). The outlined procedure can be summarized as follows: Initially, the literature plain text file that has been downloaded is imported into the CiteSpace software for the purpose of conducting the literature deduplication and format conversion process, resulting in the acquisition of the processed data file. Subsequently, the software’s pertinent parameters were set and the desired modules (e.g., authors, institutions, country (first author), journal, keywords, references) were selected for analysis, followed by the execution of the software to generate visual outcomes. Finally, the parameters were fine-tuned to enhance the reliability and clarity of the visual graphics [20,21]. Betweenness centrality (BC) serves as an indicator of the potential for any shortest path through a node within a network [22]. The BC of node directly correlates with the significance of a node within the network. Particularly in networks with weak connections, nodes exhibiting higher BC may assume pivotal roles as connectors. The quantities of nodes (N) and edges (E) represent crucial metrics that reflect the scale of the network [20]. Modularity (Q) serves as an indicator for assessing the internal structure of the network. A higher Q value indicates a more pronounced clustering effect within the network. Notably, a Q value exceeding 0.3 signifies a significant clustering structure within the network [23]. Similarly, the weighted mean silhouette (S) is employed as an indicator to ascertain the homogeneity of network clustering. A proximity to 1 on the S value denotes a high level of homogeneity within the network, clustering outcomes are deemed persuasive when the S value surpasses 0.7 [24].

3. Results

3.1. Overall Research Overview

The quantification of publications can serve as a rough indicator for estimating the popularity and development of a certain research domain within a given historical period. As depicted in Figure 2, the annual and cumulative publication number of publications pertaining to the adaptive evolution of marine organisms are presented. Over the past two decades, there has been an increase in annual publications. Notably, between 2003 and 2016, the annual publication counts experienced a slow rise, ranging from 13 to approximately 30 publications, but the average annual publication count did not surpass 18 publications. Since 2017, the field has experienced a period of rapid development, evidenced by an annual publication count exceeding 45 publications, peaking at 69 publications in 2022. Despite a temporary increase followed by a decline between 2019 and 2020, the overarching trend in the number of publications has exhibited a rapid growth. This trend displays the heightened interest among researchers in the adaptive evolution of marine organisms, which has progressively emerged as a popular area of investigation. Notably, the most frequently cited documents within this domain include “Microplastic in marine organism: Environmental and toxicological effects” (372 citations) [25] and “Genomic signatures of local directional selection in a high gene flow marine organism; the Atlantic cod (Gadus morhua)” (185 citations) [26].
Based on a comprehensive survey examining the global distribution of publications, China has emerged as the leading country in terms of the quantity of publications pertaining to the adaptive evolution of marine life, with a total of 272 publications, constituting 41.27% of the overall publications. Following China is the USA, which has contributed 120 publications, accounting for 18.21% of the total. Germany secures the third position with 55 articles published. The top 10 countries actively engaged in publishing articles within this domain are presented in Table 1.
A comprehensive examination of the literature sources reveals that research pertaining to the adaptive evolution of marine organisms is published across a wide range of 238 scholarly journals, including journals such as Frontiers in Marine Science, Zootaxa, Marine Pollution Bulletin and Journal of Oceanology and Limnology. Notably, Table 2 provides an enumeration of the ten most prolific journals in terms of research output within this domain. Specifically, a cumulative number of 205 publications, constituting 31.11% of the overall literature, are published in these ten journals. Among the journals in Table 2 (namely Frontiers in Marine Science, Marine Pollution Bulletin, and Molecular Phylogenetics and Evolution), they are categorized into Q1 JCR quartile, exhibiting comparatively higher impact factors. In Frontiers in Marine Science, 43 articles have been published, meaning it is the journal with the most articles published. A small number of professional core journals publish most of the professional literature, while some studies are often scattered across journals of different disciplines.

3.2. Cooperation Network Analysis

3.2.1. Country Cooperation Network

The distribution of the literature output and cooperation relationships between countries is visually represented by the national cooperation network. Figure 3 illustrates the national cooperation network pertaining to research on the adaptive evolution of marine organisms. The size of each node corresponds to the quantity of relevant documents originating from a particular country, while the thickness of the lines connecting countries indicates the level of collaboration between them. Additionally, the darkness of the color signifies the earliness of the publications. The analysis presented in Figure 3 reveals a notable level of proximity in the cooperation among countries, particularly among European nations like Spain, Russia, and France (BC > 0.1), which have conducted early engagement in research pertaining to the adaptive evolution of marine organisms. Despite China’s prolific publication output in this domain, its involvement commenced at a later stage, resulting in a more autonomous research approach with limited international collaboration.

3.2.2. Research Institution Cooperation Network

Figure 4 illustrates the cooperation network among international research institutions. The network density is 0.0191, including 260 nodes and 644 edges. The network exhibits a density of 0.0191, comprising 260 nodes and 644 edges. Notably, four institutions, namely the Chinese Academy of Sciences (208 articles, BC = 0.1), Institute of Oceanology (194 articles, BC = 0.1), University of Chinese Academy of Sciences (130 articles, BC = 0), and Laoshan Laboratory (103 articles, BC = 0.09), have contributed significantly to the study of adaptive evolution in marine organisms, with a minimum of 100 publications each. These four institutions collectively accounted for the highest number of publications in this field, surpassing 70% of the total publications worldwide by all institutions. However, the level of cooperation network among these four institutions is relatively low, with a BC value not exceeding 0.1. On the contrary, despite the limited number of publications from institutions such as UDICE-French Research Universities (17 articles, BC = 0.17), Center National de la Recherche Science (18 articles, BC = 0.17), and University of California System (5 articles, BC = 0.11), their collaborative network exhibits a higher degree of connectivity. This observation highlights the significant role played by these institutions in fostering international cooperation within this field.

3.2.3. Author Cooperation Network

The utilization of author collaboration network analysis can serve as a valuable tool for visualizing prominent research teams and scholars within a certain academic field. As depicted in Figure 5, the collaboration network of authors specializing in the adaptive evolution of marine organisms exemplifies a cooperative structure. A series of investigations conducted by researchers in this field has contributed to the establishment of a relatively distinct author collaboration network. However, it is worth noting that the level of collaboration among authors remains modest (N = 291, E = 281, density = 0.0067), necessitating the enhancement of international cooperation among authors. Among the authors, a mere eight scholars have published a minimum of 10 articles in this particular field. Specifically, Xu Kuidong (Chinese Academy of Sciences Inst Oceanol, 37 articles) and Li Xinzheng (Chinese Academy of Sciences Inst Oceanol, 25 articles) stand out with their publication of 37 and 25 articles, respectively, surpassing the threshold of 20 articles. This evidence suggests their active involvement in this field, positioning them at the forefront globally in terms of publication volume. Additionally, our analysis reveals that the nodes of these two authors are interconnected with four other authors Zhan Zifeng (Chinese Academy of Sciences Inst Oceanol, 10 articles), Dong Dong (Chinese Academy of Sciences Inst Oceanol, 5 articles), Sui Jixing (Chinese Academy of Sciences Inst Oceanol, 5 articles), and Kou Qi (Chinese Academy of Sciences Inst Oceanol, 4 articles). Moreover, there exist more node connections among them, which signify a significant level of collaboration among these six authors. Based on a comparative analysis of the institutions of the authors and their published articles, it can be deduced that six scholars, under the leadership of Xu Kuidong and Li Xinzheng, have established two research teams that hold significant global influence in the domain of adaptive evolution of marine organisms. Their primary focus lay in investigating marine biodiversity and biological adaptability across diverse habitats. Despite a delayed initiation, Chinese scholars have progressively attained substantial outcomes in this research domain, culminating in the formation of specific research teams. This development signified the gradual emergence of Chinese scholars as frontrunners in this field.

3.3. Keyword Analysis Cooperation Network

Keywords are concise words that effectively represent the subject and substance of an article. The utilization of keyword co-occurrence and cluster analysis aids in the swift identification of prominent subjects within a specific research domain. Figure 6 and Figure 7 display the outcomes of keyword co-occurrence and clustering analysis within the field of adaptive evolution in marine organisms. The resulting keyword co-occurrence and clustering network exhibit a relatively sparse structure (N = 286, E = 363, density = 0.0089). To ensure conciseness and clarity, only keywords with frequencies equal to or exceeding 5 are highlighted in Figure 6. Among the 287 identified keywords, 28 keywords exhibit a frequency equal to or exceeding 10, while 21 keywords display a frequency ranging between 10 and 20. Additionally, 7 keywords demonstrate a frequency surpassing 20. Collectively, these keywords manifest conspicuous nodes and exhibit dense line connections. Notably, 12 keywords possess a BC value equal to or exceeding 0.1, including diversity (36 times, BC = 0.21), fish (26 times, BC = 0.27), sea (24 times, BC = 0.18), patterns (18 times, BC = 0.14), growth (17 times, BC = 0.13), adaptive evolution (14 times, BC = 0.13), marine (14 times, BC = 0.17), abundance (12 times, BC = 0.14), biological invasions (11 times, BC = 0.14), and bay (11 times, BC = 0.16). These keywords exhibit both high frequency and connectivity, and they also align well with the research topic of “adaptive evolution of marine organisms”. Furthermore, in addition to the aforementioned 10 high-frequency keywords, the remaining two keywords, namely dispersal (BC = 0.15) and gulf (BC = 0.10), are identified.
The utilization of cluster analysis based on a keyword co-occurrence network reveals the keyword clustering within the research field of marine organism adaptive evolution, as depicted in Figure 7. The observed keyword clustering structure exhibits statistical significance (Q = 0.8317 > 0.3), and the obtained clustering outcomes are highly persuasive (S = 0.9381 > 0.7). Figure 7 illustrates a total of 10 cluster labels, arranged in ascending order of cluster numbers, which are as follows: adaptive evolution (cluster #0), species richness (cluster #1), phylogenetic analysis (cluster #2), ocean acidification (cluster #3), health risk (cluster #4), DNA barcoding (cluster #5), differential gene expression (cluster #6), Crassostrea gigas (cluster #7), biological invasions (cluster #8), and marine organisms (cluster #9).

3.4. Keyword Timeline Network Analysis

The utilization of the keyword timeline facilitates a deeper comprehension of the chronological progression and advancement of distinct keyword clusters within the realm of adaptive evolution in marine organisms. In order to illustrate this, a keyword timeline network analysis was undertaken on clustered keywords spanning from 2003 to 2023, as depicted in Figure 8. The outcome of this analysis reveals the existence of 10 keyword clusters, wherein keywords belonging to the same cluster are presented on a shared timeline based on their respective time of occurrence. Cluster#5 had the shortest duration, while Cluster#1 exhibits the longest duration, thereby suggesting that the investigation of species richness had consistently been a prominent research direction in the field of adaptive evolution of marine organisms; Cluster#8 garners considerable attention in initial stages, but this trend had stagnated before 2020; despite commencing later, Cluster#0 remains an active and significant research topic within the field of adaptive evolution of marine organisms.

3.5. Keywords Burst Citation

The abrupt surge and burst of a specific keyword within a short timeframe can serve as an indicator of a frontier hot spot within the realm of research. Through the identification of keywords that experience rapid proliferation, we can effectively observe and comprehend the alterations in research topics that have transpired in recent years, employing the analysis of keyword burst detection. In our study on the adaptive evolution of marine organisms spanning from 2003 to 2023, we constructed a network of keyword co-occurrence and successfully detected the top 25 keywords exhibiting the strongest citation burst, as depicted in Table 3. The keyword “performance” exhibits the strongest burst citation strength (strength = 6.21) and is characterized by a relatively brief duration (2020–2021). “Accumulation” is the keyword with the earliest outbreak time (strength = 2.43). The majority of keywords experience a gradual burst after 2016, while only seven keywords (accumulation, adaptive evolution, new species, expression, mediterranean sea, ocean acidification and bay) exhibit burst patterns prior to 2016, aligning with the trend of a limited number of publications during that period.

4. Discussion

4.1. Trends in the Number of Publications

The ocean harbors abundant biological resources and holds a significant role in global biodiversity. Marine organisms undergo diverse adaptations through the interplay of genetic variation, drift, and natural selection. Adaptive evolution refers to the dynamic process by which organisms respond to external environmental pressures [27]. Despite the ocean’s unique and intricate environment serving as a crucial catalyst for the adaptive evolution of marine organisms [28], the underlying mechanisms governing this process remain unclear. In recent years, the advancement of high-throughput sequencing technology and gene editing technology has facilitated extensive investigations in the domain of adaptive evolution, thereby significantly propelling forward the progress of adaptive evolution research on marine organisms [29,30]. The research on adaptive evolution in marine organisms can be categorized into two distinct phases: the initial stage spanning from 2003 to 2016, and the stage of rapid development spanning from 2017 to 2023. Despite a delayed initiation in 2016, the research in this field has witnessed exponential growth in publications owing to the remarkable expansion of research resources, funding opportunities, scholarly engagement, extensive scientific collaboration, and the emergence of diverse disciplines such as biogeography, phylogeny, and population genetics [31,32,33]. This trend underscores the increasing significance and widespread interest in investigating the adaptive evolution of marine organisms, which has emerged as a prominent research hotspot.

4.2. Scientific Contributions

Through the utilization of cooperative network analysis encompassing countries, institutions, and authors, it becomes possible to summarize the worldwide research prowess pertaining to the adaptive evolution of marine organisms. Moreover, this approach facilitates understanding of the institutions and research groups with academic influence, as well as the collaborative relationships among them. Notably, China has emerged at a dominant position in terms of research output and publication count within this domain, while also exhibiting distinct research directions. Zhang et al. (2023) [34] have examined the adaptation of two closely related oysters inhabiting distinct areas (intertidal and estuarine) to varying salinity environments. The research has elucidated the adaptive mechanisms employed by these marine mollusks, while also evaluating their capacity for adaptive evolution under climate change. Zhao et al. (2018) [35] and Li et al. (2020) [36] have conducted research on the population genetic differentiation and adaptive evolution of Chinese Sea Bass (Lateolabrax maculatus) and Pacific Oyster (Crassostrea gigas), respectively, shedding light on the population differentiation and local adaptation of these two organisms. Concerns have been raised by scholars regarding the ocean environment, as their research has revealed the presence of microplastics, nanoparticles, heavy metals, and pesticides that remain in marine organisms [37,38,39,40]. These residues have the potential to induce toxicity in marine organisms and elicit adaptive stress responses. Nonetheless, the realm of domestic research exhibits relative independence with limited engagement in international collaboration. Nevertheless, certain European nations have demonstrated a greater inclination towards cooperation in this domain. Scholars hailing from France and Spain have examined the adaptive evolutionary traits of Corallium rubrum amidsted climate change, encompassing pivotal genes such as heat shock protein70 and genetic selection, which significantly influenced individual adaptation [41]. In addition, as a result of the diverse hydrological conditions present in the Baltic Sea, researchers have identified various mechanisms through which invasive species and new cryptic species were introduced and adapted to the local environment. The research was collaboratively initiated and conducted by research institutions in Estonia, Lithuania, Sweden, and Russia [42]. Notably, Chinese research institutions, including Chinese Academy of Sciences and Ocean University of China, have made significant contributions to the field of adaptive evolution of marine organisms, constituting a substantial proportion of the overall research efforts. Similarly, the author’s collaborative network analysis can be employed to visually represent prominent research teams and scholars within a specific academic domain. The two teams, under the leadership of Li Xinzheng and Xu Kuidong, are affiliated with the Chinese Academy of Sciences Institute of Oceanology, exhibiting a high level of cooperation and concentrating their efforts on the exploration of marine biodiversity and biological adaptability across diverse habitats [43,44]. Chinese scholars have increasingly garnered contributions in the domain of adaptive evolution of marine organisms, despite their relatively delayed entry into the field. Their fruitful results and the establishment of robust research teams have positioned Chinese scholars as emerging leaders in this area.

4.3. Research Hotspots

Keywords serve as highly condensed words of the paper, effectively conveying its theme and content. Employing keyword co-occurrence and clustering analysis facilitates the rapid identification of prominent hotspots within a research domain. In the realm of adaptive evolution of marine organisms, the prevailing research interests revolve around adaptive evolution, species richness, phylogenetic analysis, and health risk. Notably, “adaptive evolution” stood as the most pertinent keyword. Provanna glabra, as a dominant species inhabiting hydrothermal vents and cold seeps in the deep sea, exhibited significant genetic variations local adaptability. These findings are likely attributed to geographic barriers, constrained diffusion capacity and diverse pressures imposed by deep-sea environments [45]. Similarly, Perez et al. (2023) [46] discovered that gene methylation in deep-sea polychaetes contributes to the expression of housekeeping gene, thereby facilitating their adaptation to the deep-sea environment. Notably, DNA methylation played a significant role in this adaptive process. “Species richness” is the most frequently keyword. It is widely acknowledged that coral serve as vital ecosystems, providing habitat for many marine organisms, and exhibiting a high level of species richness. However, there is limited knowledge regarding the factors influencing the diversity and abundance of coral symbionts. In their study, Britayev et al. (2023) [47] discovered that the size and density of colony had an impact on the diversity and abundance of coral symbionts, and that species competitiveness escalated with the increasing of abundance. Since 2004, the utilization of phylogenetic analysis has been an essential analytical approach for marine biodiversity and classification research. Asahida et al. (2004) [48] employed microsatellite locus to determine the base substitution rate of rockfishes from various geographical regions. The findings from the phylogenetic tree analysis revealed notable variations in DNA sequences, indicating a distinct evolutionary history compared to North American species. In a recent investigation, the rearrangement and phylogenetic analysis of Ophiuroidea species’ mitogenomes unveiled the differentiation of ophiuroids during the Permian-Triassic mass extinction event with deep-sea lineages differentiating in the Cretaceous period [49]. The immune response and adaptive evolution of numerous marine organisms occur within harsh habitats. In the research conducted by Saidani et al. (2019) [50], clams were subjected to photocatalyst nanocomposites (TiO2NPs and AuTiO2NPs) to simulate the impact on marine ecology. The researchers observed the accumulation of Au and Ti in the gill and digestive glands of the clams, which subsequently led to a reduction in their filtration and respiration rates. Consequently, the clams exhibited alterations in their adaptive behavior towards the environment. In general, the investigation of phylogeny, biodiversity, and adaptive immunity in marine organisms represent prominent research hotspots within the field of marine organism adaptive evolution.

4.4. Hotspots Evolution and Research Frontiers

The utilization of a keyword timeline proves advantageous in gaining a deeper comprehension of the evolution and development process of diverse keywords within the realm of adaptive evolution pertaining to marine organisms. The cluster “species richness” emerges as the most extensive, encompassing the time period spanning from 2003 to 2023. It represents the earliest and most enduring direction of investigation within the field of marine biological research and development. Initially, scholars focused their research on the recognition and identification of novel species [51,52]. However, as a consequence of potential anthropogenic pressure and climate change within marine ecosystems, an increasing number of researchers have directed their attention towards the issue of invasive species [53]. In a rapid assessment survey, Minchin et al. (2017) [54] employed the abundance and distribution range approach to revealed a potential correlation between the intrusion of Asian kelp Undaria pinnatifida and seawater salinity. Due to the growing demand for seafood and the extensive utilization of molecular technology and DNA barcoding, researchers primarily focused on examining the genetic diversity and population structure of economically significant marine species. Additionally, they aimed to forecast the interaction between system geography history and population history through the identification of haplotype diversity within biological populations [55]. Furthermore, several researchers have conducted inquiries into the impact of geomorphological characteristics on marine biodiversity, revealing that sediment depth, temperature, and nutrient levels played a crucial role in shaping gene flow and population differentiation [56]. Nevertheless, the short application period of DNA barcoding technology could be attributed to the limitation posed by a single gene fragment and the low mutation frequency [57].
Several keywords experience burst during a specific historical period, such as “adaptive evolution” and “ocean acidification”, suggesting that these keywords garner significant interest during the year of burst. However, with the continuous update and iteration of the research, the attention towards these keywords gradually diminished, ultimately leading to the ending of their burst. The discussion surrounding the population structure and adaptive evolution of marine organisms had garnered significant attention from scholars due to the escalating impact on the marine environment, including ocean acidification [58]. Consequently, the keywords “phylogeny” and “identification” have emerged as new frontier hotspots within this domain. The sudden burst in the frequency of the keyword “deep sea” within a brief timeframe indicates that the field of deep-sea biology has become a prominent frontier hotspot, likely attributable to the rapid advancements in submersible [59].

5. Conclusions

This review conducts a bibliometric analysis of the literatures pertaining to the adaptive evolution of marine organisms from 2003 to 2023, utilizing CiteSpace. The analysis focuses on the fundamental characteristics and sources of the retrieved literature from the aspects of annual publications and journals. Notably, there has been a significant increase in the number of publications within this domain over the past two decades. Subsequently, through the establishment of a collaborative network involving countries, institutions, and authors of the publications, it became evident that domestic research is more independent and some European countries cooperate more tightly. Ultimately, the utilization of keyword co-occurrence, time evolution, and burst detection analysis enable the comprehensive synthesis of the impacts pertaining to adaptive evolution, species richness, systematic evolution analysis and toxicity immunity of marine organisms. This analysis has served to elucidate the research frontiers, offering novel perspectives on the frontier hotspots and trends within this domain. Therefore, it is recommended that future research should be carried out from the following aspects: (1) Despite the significant contributions made by domestic research in the realm of adaptive evolution of marine organisms, it remains largely independent and lacks global collaboration. Domestic scholars should enhance cooperation and collaboration among institutions to further investigate the mechanism of adaptive evolution; Simultaneously, foreign scholars are encouraged to seize the opportunity of rapid changes in the marine environment, thereby expanding research outcomes and making substantial contributions to the global of marine biological adaptability. (2) The research on DNA barcoding commences belatedly and exhibits inadequate persistence, thereby enabling researchers to develop conserved DNA barcoding applications in marine biological adaptability. (3) In recent times, studies pertaining to “biological invasion” have become infrequent, while the exploration of the deep sea and phylogeny has emerged as novel frontier hotspots. Researchers can intensify their exploration of marine organism resources, encompassing remote sensing and in situ investigation, to unveil additional mechanisms of invasive alien species. Given the ongoing discovery of novel species, it is imperative to promptly update phylogenetic research on marine organisms. However, the research resources available for deep-sea organisms remain constrained. Consequently, it is crucial to actively advance the development of deep-sea equipment and technology to safeguard the in situ data of deep-sea samples, thereby enabling more precise analyses of biological research. This will facilitate the examination in adaptive characteristics, distribution patterns and evolution pathways of marine organisms, further investigating the molecular basis underlying character diversity and elucidating the biological endogenous mechanism.

Author Contributions

Conceptualization, L.M., J.H. and N.M.; methodology, N.M., Y.Z. and J.W.; software, Y.Z. and N.M.; writing—original draft preparation, N.M.; writing—review and editing, L.M. and X.S.; project administration, X.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of China, grant number #41876147. the Jiangsu Provincial Basic Research Program (Natural Science Foundation) Distinguished Youth Fund Project, grant number BK20190048. Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF) [CX (22) 2032], and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors of this paper express their most sincere gratitude to all the staff who helped during our experiment and writing.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brandon, R.N. Adaptation and evolutionary theory. Stud. Hist. Philos. Sci. 1978, 9, 181–206. [Google Scholar] [CrossRef]
  2. Ackerly, D.D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 2003, 164, S165–S184. [Google Scholar] [CrossRef]
  3. Yuan, T.P.; Huang, Y.P.; Miao, S.Y.; Li, L.; Yan, Y. Genetic diversity and population structure of Capitulum mitella (Cirripedia: Pedunculata) in China inferred from mitochondrial DNA sequences. Biochem. Syst. Ecol. 2016, 67, 22–28. [Google Scholar] [CrossRef]
  4. Korn, O.M.; Golubinskaya, D.D.; Rees, D.J.; Glenner, H.; Høeg, J.T. Phylogenetic position, complete larval development and larval sexual dimorphism in a rhizocephalan barnacle, Lernaeodiscus rybakovi sp. nov. (Cirripedia: Rhizocephala: Peltogastridae), parasitizing the crab Pachycheles stevensii Stimpson, 1858 (Decapoda: Anomura: Porcellanidae). Zool. Anz. 2020, 287, 178–197. [Google Scholar]
  5. Lin, H.C.; Cheang, C.C.; Corbari, L.; Chan, B.K.K. Trans-Pacific genetic differentiation in the deep-water stalked barnacle Scalpellum stearnsii (Cirripedia: Thoracica: Scalpellidae). Deep Sea Res. Part I Oceanogr. Res. Pap. 2020, 164, 103359. [Google Scholar] [CrossRef]
  6. Zhang, W.Y.; Storey, K.B.; Dong, Y.W. Adaptations to the mudflat: Insights from physiological and transcriptional responses to thermal stress in a burrowing bivalve Sinonovacula constricta. Sci. Total Environ. 2020, 710, 136280. [Google Scholar] [CrossRef]
  7. Chan, B.K.; Dreyer, N.; Gale, A.S.; Glenner, H.; Ewers-Saucedo, C.; Pérez-Losada, M.; Kolbasov, G.A.; Crandall, K.A.; Høeg, J.T. The evolutionary diversity of barnacles, with an updated classification of fossil and living forms. Zool. J. Linn. Soc. 2021, 193, 789–846. [Google Scholar] [CrossRef]
  8. Yévenes, M.; Núñez-Acuña, G.; Gallardo-Escárate, C.; Gajardo, G. Adaptive mitochondrial genome functioning in ecologically different farm-impacted natural seedbeds of the endemic blue mussel Mytilus chilensis. Comp. Biochem. Physiol. Part D Genom. Proteom. 2022, 42, 100955. [Google Scholar] [CrossRef]
  9. Li, J.Y.; Song, Z.L.; Yan, G.Y.; He, L.S. The complete mitochondrial genome of the largest amphipod, Alicella gigantea: Insight into its phylogenetic relationships and deep sea adaptive characters. Int. J. Biol. Macromol. 2019, 141, 570–577. [Google Scholar] [CrossRef]
  10. Wang, C.; Zhang, Z.H.; Deng, Z.T.; Sun, J.F.; Zhao, R.Y.; Cui, Y.T.; Wang, R.J.; Li, Y.Q.; Wang, Z.K. Classical aquaporins from Pacific white shrimp (Litopenaeus vannamei): Molecular characterization and expression analysis in hypersalinity. Aquac. Rep. 2022, 23, 101016. [Google Scholar] [CrossRef]
  11. Wang, Y.X.; Lin, S.R.; Xu, L.Z.; Ye, Y.Y.; Qi, P.Z.; Wang, W.F.; Buttino, I.; Li, H.F.; Guo, B.Y. Comparative transcriptomic analysis revealed changes in multiple signaling pathways involved in protein degradation in the digestive gland of Mytilus coruscus during high-temperatures. Comp. Biochem. Physiol. Part D Genom. Proteom. 2023, 46, 101060. [Google Scholar] [CrossRef]
  12. Bettencourt, R.; Pinheiro, M.; Egas, C.; Gomes, P.; Afonso, M.; Shank, T.; Santos, R.S. High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus. BMC Genom. 2010, 11, 559. [Google Scholar] [CrossRef]
  13. Sun, J.; Zhang, Y.; Xu, T.; Zhang, Y.; Mu, H.W.; Zhang, Y.J.; Lan, Y.; Fields, C.J.; Hui, J.H.L.; Zhang, W.P. Adaptation to deep-sea chemosynthetic environments as revealed by mussel genomes. Nat. Ecol. Evol. 2017, 1, 0121. [Google Scholar] [CrossRef]
  14. Wong, Y.H.; Sun, J.; He, L.S.; Chen, L.G.; Qiu, J.W.; Qian, P.Y. High-throughput transcriptome sequencing of the cold seep mussel Bathymodiolus platifrons. Sci. Rep. 2015, 5, 16597. [Google Scholar] [CrossRef]
  15. Zhang, Y.J.; Sun, J.; Chen, C.; Watanabe, H.K.; Feng, D.; Zhang, Y.; Chiu, J.M.; Qian, P.Y.; Qiu, J.W. Adaptation and evolution of deep-sea scale worms (Annelida: Polynoidae): Insights from transcriptome comparison with a shallow-water species. Sci. Rep. 2017, 7, 46205. [Google Scholar] [CrossRef]
  16. Gan, Z.B.; Yuan, J.B.; Liu, X.M.; Dong, D.; Li, F.H.; Li, X.Z. Comparative transcriptomic analysis of deep-and shallow-water barnacle species (Cirripedia, Poecilasmatidae) provides insights into deep-sea adaptation of sessile crustaceans. BMC Genom. 2020, 21, 240. [Google Scholar] [CrossRef]
  17. Lan, Y.; Sun, J.; Tian, R.M.; Bartlett, D.H.; Li, R.S.; Wong, Y.H.; Zhang, W.P.; Qiu, J.W.; Xu, T.; He, L.S. Molecular adaptation in the world’s deepest-living animal: Insights from transcriptome sequencing of the hadal amphipod Hirondellea gigas. Mol. Ecol. 2017, 26, 3732–3743. [Google Scholar] [CrossRef]
  18. Liang, L.Y.; Chen, J.W.; Li, Y.N.; Zhang, H.B. Insights into high-pressure acclimation: Comparative transcriptome analysis of sea cucumber Apostichopus japonicus at different hydrostatic pressure exposures. BMC Genom. 2020, 21, 68. [Google Scholar] [CrossRef]
  19. Chen, C.M. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2006, 57, 359–377. [Google Scholar] [CrossRef]
  20. Chen, C.M. CiteSpace: A Practical Guide for Mapping Scientific Literature; Nova Science Publishers: Hauppauge, NY, USA, 2016; pp. 1–190. [Google Scholar]
  21. Chen, C.M. Science mapping: A systematic review of the literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar] [CrossRef]
  22. Freeman, L.C. A Set of Measures of Centrality Based on Betweenness. Sociometry 1977, 40, 35–41. [Google Scholar] [CrossRef]
  23. Newman, M.E.J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 2006, 103, 8577–8582. [Google Scholar] [CrossRef]
  24. Peter, R.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar]
  25. Guzzetti, E.; Sureda, A.; Tejada, S.; Faggio, C. Microplastic in Marine Organism: Environmental and Toxicological Effects. Environ. Toxicol. Pharmacol. 2018, 64, 164–171. [Google Scholar] [CrossRef] [PubMed]
  26. Nielsen, E.E.; Hemmer-Hansen, J.; Poulsen, N.A.; Loeschcke, V.; Carvalho, G.R. Genomic signatures of local directional selection in a high gene flow marine organism; The Atlantic cod (Gadus morhua). BMC Evol. Biol. 2009, 9, 276. [Google Scholar] [CrossRef] [PubMed]
  27. Mayr, B.E. The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Belknap Press of Harvard University Press: Cambridge, MA, USA, 1982; Volume 60, pp. 1–974. [Google Scholar]
  28. Poulton, S.W.; Canfield, D.E. Ferruginous Conditions: A Dominant Feature of the Ocean through Earth’s History. Elements 2011, 7, 107–112. [Google Scholar] [CrossRef]
  29. He, S.; Del Viso, F.; Chen, C.Y.; Ikmi, A.; Kroesen, A.E.; Gibson, M.C. An axial Hox code controls tissue segmentation and body patterning in Nematostella vectensis. Science 2018, 361, 1377–1380. [Google Scholar] [CrossRef]
  30. Martin, A.; Serano, J.; Jarvis, E.; Bruce, H.; Wang, J.; Ray, S.; Barker, C.; O’Connell, L.; Patel, N. CRISPR/Cas9 Mutagenesis Reveals Versatile Roles of Hox Genes in Crustacean Limb Specification and Evolution—ScienceDirect. Curr. Biol. 2016, 26, 14–26. [Google Scholar] [CrossRef]
  31. Cowman, P.F.; Bellwood, D.R. The historical biogeography of coral reef fishes: Global patterns of origination and dispersal. J. Biogeogr. 2013, 40, 209–224. [Google Scholar] [CrossRef]
  32. Zattara, E.E.; Bely, A.E. Phylogenetic distribution of regeneration and asexual reproduction in Annelida: Regeneration is ancestral and fission evolves in regenerative clades. Invertebr. Biol. 2016, 135, 400–414. [Google Scholar] [CrossRef]
  33. Shen, Y.J.; Kou, Q.; Chen, W.T.; He, S.P.; Yang, M.; Li, X.Z.; Gan, X.N. Comparative population structure of two dominant species, Shinkaia crosnieri (Munidopsidae: Shinkaia) and Bathymodiolus platifrons (Mytilidae: Bathymodiolus), inhabiting both deep-sea vent and cold seep inferred from mitochondrial multi-genes. Ecol. Evol. 2016, 6, 3571–3582. [Google Scholar] [CrossRef]
  34. Zhang, Z.Y.; Li, A.; She, Z.C.; Wang, X.G.; Jia, Z.; Wang, W.; Zhang, G.F.; Li, L. Adaptive divergence and underlying mechanisms in response to salinity gradients between two Crassostrea oysters revealed by phenotypic and transcriptomic analyses. Evol. Appl. 2023, 16, 234–249. [Google Scholar] [CrossRef]
  35. Zhao, Y.F.; Peng, W.Z.; Guo, H.Y.; Chen, B.H.; Zhou, Z.X.; Xu, J.; Zhang, D.C.; Xu, P. Population genomics reveals genetic divergence and adaptive differentiation of Chinese sea bass (Lateolabrax maculatus). Mar. Biotechnol. 2018, 20, 45–59. [Google Scholar] [CrossRef]
  36. Li, A.; Li, L.; Wang, W.; Zhang, G.F. Acetylome analysis reveals population differentiation of the pacific oyster Crassostrea gigas in response to heat stress. Mar. Biotechnol. 2020, 22, 233–245. [Google Scholar] [CrossRef]
  37. Qu, J.L.; Wu, P.F.; Pan, G.J.; Li, J.P.; Jin, H.B. Microplastics in seawater, sediment, and organisms from Hangzhou Bay. Mar. Pollut. Bull. 2022, 181, 113940. [Google Scholar] [CrossRef]
  38. Zhu, X.S.; Zhou, J.; Cai, Z.H. The toxicity and oxidative stress of TiO2 nanoparticles in marine abalone (Haliotis diversicolor supertexta). Mar. Pollut. Bull. 2011, 63, 334–338. [Google Scholar] [CrossRef]
  39. Zhang, L.; Shi, Z.; Jiang, Z.J.; Zhang, J.P.; Wang, F.; Huang, X.P. Distribution and bioaccumulation of heavy metals in marine organisms in east and west Guangdong coastal regions, South China. Mar. Pollut. Bull. 2015, 101, 930–937. [Google Scholar] [CrossRef]
  40. Li, H.J.; Ye, S.; Ye, J.Q.; Fan, J.F.; Gao, M.L.; Guo, H. Baseline survey of sediments and marine organisms in Liaohe Estuary: Heavy metals, polychlorinated biphenyls and organochlorine pesticides. Mar. Pollut. Bull. 2017, 114, 555–563. [Google Scholar] [CrossRef]
  41. Haguenauer, A.; Zuberer, F.; Ledoux, J.B.; Aurelle, D. Adaptive abilities of the Mediterranean red coral Corallium rubrum in a heterogeneous and changing environment: From population to functional genetics. J. Exp. Mar. Biol. Ecol. 2013, 449, 349–357. [Google Scholar] [CrossRef]
  42. Ojaveer, H.; Olenin, S.; Narščius, A.; Florin, A.-B.; Ezhova, E.; Gollasch, S.; Jensen, K.R.; Lehtiniemi, M.; Minchin, D.; Normant-Saremba, M. Dynamics of biological invasions and pathways over time: A case study of a temperate coastal sea. Biol. Invasions 2017, 19, 799–813. [Google Scholar] [CrossRef]
  43. Xu, Y.; Ma, L.; Sui, J.X.; Li, X.Z.; Wang, H.F.; Zhang, B.L. Potential impacts of climate change on the distribution of echinoderms in the Yellow Sea and East China Sea. Mar. Pollut. Bull. 2023, 194, 115246. [Google Scholar] [CrossRef]
  44. Qu, Y.; Xu, K.D.; Li, T.; Wang, M.Y.; Zhong, H.; Chen, T.Y. Deep-sea coral evidence for dissolved mercury evolution in the deep North Pacific Ocean over the last 700 years. J. Oceanol. Limnol. 2021, 39, 1622–1633. [Google Scholar] [CrossRef]
  45. Zhang, Y.; Cheng, J.; Sha, Z.L.; Hui, M. Population genetic structure and implication for adaptive differentiation of the snail (Gastropoda, Provannidae) in deep-sea chemosynthetic ecosystems. Zool. Scr. 2023, online. [Google Scholar] [CrossRef]
  46. Perez, M.; Aroh, O.; Sun, Y.N.; Lan, Y.; Juniper, S.K.; Young, C.R.; Angers, B.; Qian, P.Y. Third-generation sequencing reveals the adaptive role of the epigenome in three deep-sea polychaetes. Mol. Biol. Evol. 2023, 40, msad172. [Google Scholar] [CrossRef]
  47. Britayev, T.A.; Petrochenko, R.A.; Burmistrova, Y.A.; Nguyen, T.H.; Lishchenko, F.V. Density and Bleaching of Corals and Their Relationship to the Coral Symbiotic Community. Diversity 2023, 15, 456. [Google Scholar] [CrossRef]
  48. Asahida, T.; Gray, A.K.; Gharrett, A.J. Use of microsatellite locus flanking regions for phylogenetic analysis? A preliminary study of Sebastes subgenera. Environ. Biol. Fishes 2004, 69, 461–470. [Google Scholar] [CrossRef]
  49. Sun, S.E.; Xiao, N.; Sha, Z.L. Mitogenomes provide insights into the phylogeny and evolution of brittle stars (Echinodermata, Ophiuroidea). Zool. Scr. 2023, 52, 17–30. [Google Scholar] [CrossRef]
  50. Saidani, W.; Sellami, B.; Khazri, A.; Mezni, A.; Dellali, M.; Joubert, O.; Sheehan, D.; Beyrem, H. Metal accumulation, biochemical and behavioral responses on the Mediterranean clams Ruditapes decussatus exposed to two photocatalyst nanocomposites (TiO2 NPs and AuTiO2NPs). Aquat. Toxicol. 2019, 208, 71–79. [Google Scholar] [CrossRef]
  51. Schotte, M.; Kensley, B. New species and records of Flabellifera from the Indian Ocean (Crustacea: Peracarida: Isopoda). J. Nat. Hist. 2005, 39, 1211–1282. [Google Scholar] [CrossRef]
  52. Spiridonov, V.A.; Apel, M. A new species and new records of deep-water Calappidae (Crustacea: Decapoda) from the Indian Ocean with a key to the Mursia Desmarest, 1823 species of the region. J. Nat. Hist. 2007, 41, 2851–2890. [Google Scholar] [CrossRef]
  53. Minchin, D.; Nunn, J. The invasive brown alga Undaria pinnatifida (Harvey) Suringar, 1873 (Laminariales: Alariaceae), spreads northwards in Europe. Bioinvasions Rec. 2014, 3, 57–63. [Google Scholar] [CrossRef]
  54. Minchin, D.; Nunn, J.; Murphy, J.; Edwards, H.; Downie, A. Monitoring temporal changes in the early phase of an invasion: Undaria pinnatifida (Harvey) Suringar using the abundance and distribution range method. Manag. Biol. Invasion 2017, 8, 53–60. [Google Scholar] [CrossRef]
  55. Zou, C.C.; Wang, L.J.; Kong, L.M.; Wang, Y.J.; Wu, Z.H.; Xu, J.H.; Song, A.H.; Liu, H.J.; You, F. High levels of genetic diversity and connectivity of whitespotted conger Conger myriaster in the East China Coast. Mar. Biol. 2020, 50, 47. [Google Scholar] [CrossRef]
  56. Cao, Y.F.; Lei, Y.L.; Fang, J.K.H.; Li, T.G. Molecular diversity of foraminiferal eDNA in sediments and their correlations with environmental factors from the Yellow Sea. Ecol. Indic. 2022, 142, 109294. [Google Scholar] [CrossRef]
  57. Wei, J.H.; Wu, R.X.; Xiao, Y.S.; Zhang, H.R.; Jawad, L.A.; Wang, Y.J.; Liu, J.; Al-Mukhtar, M.A. Li, 2013, Revealed by the DNA Barcoding of Pampus Fishes (Perciformes, Stromateidae). Diversity 2021, 13, 618. [Google Scholar] [CrossRef]
  58. Duarte, C.; Navarro, J.M.; Acuña, K.; Torres, R.; Manríquez, P.H.; Lardies, M.A.; Vargas, C.A.; Lagos, N.A.; Aguilera, V. Intraspecific variability in the response of the edible mussel Mytilus chilensis (Hupe) to ocean acidification. Estuaries Coasts 2015, 38, 590–598. [Google Scholar] [CrossRef]
  59. Han, Y.R.; Zhou, Y.D.; Chen, C.; Wang, Y.Y. Diversity and biogeography of scale worms in the subfamily Lepidonotopodinae (Annelida: Polynoidae) from Indian Ocean hydrothermal vents with descriptions of four new species. Zool. J. Linn. Soc. 2023. [Google Scholar] [CrossRef]
Figure 1. Flow diagram of the method.
Figure 1. Flow diagram of the method.
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Figure 2. Trends in the number of publications on adaptive evolution of marine organisms from 2003 to 2023.
Figure 2. Trends in the number of publications on adaptive evolution of marine organisms from 2003 to 2023.
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Figure 3. The country collaboration network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between countries, and the darkness of the color represents the earliness of publications.
Figure 3. The country collaboration network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between countries, and the darkness of the color represents the earliness of publications.
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Figure 4. The institution collaboration network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between institutions, and the darkness of the color represents the earliness of publications.
Figure 4. The institution collaboration network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between institutions, and the darkness of the color represents the earliness of publications.
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Figure 5. The author cooperation network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between authors, and the darkness of the color represents the earliness of publications.
Figure 5. The author cooperation network. The size of each node represents the quantity of relevant publications, the thickness of lines represents the level of collaboration between authors, and the darkness of the color represents the earliness of publications.
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Figure 6. The keyword cooperation network. The size of each node represents the frequency of relevant keywords, the lines represent the collaboration between keywords, and the darkness of the color represents the earliness of keywords.
Figure 6. The keyword cooperation network. The size of each node represents the frequency of relevant keywords, the lines represent the collaboration between keywords, and the darkness of the color represents the earliness of keywords.
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Figure 7. The clustered keywords cooperation network. The size of each node represents the frequency of relevant clustered keywords, the lines represent the collaboration between clustered keywords, and the darkness of the node color represents the earliness of keywords.
Figure 7. The clustered keywords cooperation network. The size of each node represents the frequency of relevant clustered keywords, the lines represent the collaboration between clustered keywords, and the darkness of the node color represents the earliness of keywords.
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Figure 8. The keyword timeline network. The vertical axis represents the cluster labels, the horizontal axis represents the time, the size of each node represents the frequency of relevant keywords, the lines represent the collaboration between keywords, and the darkness of the color represents the early keywords.
Figure 8. The keyword timeline network. The vertical axis represents the cluster labels, the horizontal axis represents the time, the size of each node represents the frequency of relevant keywords, the lines represent the collaboration between keywords, and the darkness of the color represents the early keywords.
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Table 1. National publication volume statistics (top 10).
Table 1. National publication volume statistics (top 10).
NumberCountryDocument QuantityPercentage (%)
1China27241.3
2USA12018.2
3Germany558.3
4Ireland538.0
5Australia527.9
6England406.1
7Italy395.9
8France395.9
9Japan355.3
10Lithuania304.6
Table 2. The statistics of journals in adaptive evolution of marine organisms (top 10).
Table 2. The statistics of journals in adaptive evolution of marine organisms (top 10).
Publication NameDocument QuantityImpact FactorJCR Quartile
Frontiers in Marine Science433.7Q1
Zootaxa400.9Q3
Marine Pollution Bulletin325.8Q1
Journal of Oceanology and Limnology231.6Q3
Mitochondrial DNA part B Resources200.5Q4
Chinese Journal of Oceanology and Limnology12//
Zookeys111.3Q2
Bioinvasions Records81.4Q3
Journal of Experimental Marine Biology and Ecology82.0Q2
Molecular Phylogenetics and Evolution84.1Q1
Table 3. Top 25 keywords with the strongest citation bursts.
Table 3. Top 25 keywords with the strongest citation bursts.
KeywordsStrengthBeginEnd2003–2023
accumulation2.4320052009▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂
bay2.1920092017▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂
adaptive evolution2.6720102016▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂
new species2.2320132014▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂
expression3.720142017▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂
mediterranean sea2.1620142018▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂
ocean acidification3.3220152017▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂
local adaptation2.4220162020▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂
dispersal3.220172019▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂
genetic differentiation2.7220172018▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂
molecular phylogeny2.2220172018▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂
evolution2.1520172018▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂
fish2.820182019▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
identification2.7420182023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃
gulf2.1320182019▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
chloroplast genome2.1320182019▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
temperature2.9520192020▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
behavior2.1220192020▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
performance6.2120202021▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂
genus4.5820202023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
crustacea2.8120202023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
morphology2.5720202023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
deep sea3.7320212023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
ocean3.2820212023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
phylogeny2.6720212023▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
Note: The blue and red represent the duration of the keywords, and the red represents the years of burst keywords.
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Mao, N.; Zhu, Y.; Wang, J.; Shen, X.; Hou, J.; Miao, L. Adaptive Evolution of Marine Organisms: A Bibliometric Analysis Based on CiteSpace. Water 2023, 15, 4162. https://doi.org/10.3390/w15234162

AMA Style

Mao N, Zhu Y, Wang J, Shen X, Hou J, Miao L. Adaptive Evolution of Marine Organisms: A Bibliometric Analysis Based on CiteSpace. Water. 2023; 15(23):4162. https://doi.org/10.3390/w15234162

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Mao, Ning, Ye Zhu, Junyue Wang, Xin Shen, Jun Hou, and Lingzhan Miao. 2023. "Adaptive Evolution of Marine Organisms: A Bibliometric Analysis Based on CiteSpace" Water 15, no. 23: 4162. https://doi.org/10.3390/w15234162

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