DOI: 10.46460/ijiea.1926623 ISSN: 2587-1943

Data-Driven Mapping of the Morphology-to-Molecular Transition in Symphyta (Hymenoptera): A Bibliometric and Knowledge-Network Analysis (1990–2025)

Sevda Hastaoğlu Örgen
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Symphyta, representing an early-diverging assemblage within Hymenoptera and comprising approximately 9,000 described species worldwide, has historically been studied through morphology-based taxonomy and faunistics. However, the rapid expansion of molecular techniques, computational phylogenetics, and biodiversity data integration has fundamentally transformed the research landscape of this group. This study presents a data-driven bibliometric and knowledge-network analysis of global Symphyta research based on 1,060 publications indexed in the Web of Science Core Collection between 1990 and 2025. Using CiteSpace-based science mapping, the study examines annual publication trends, collaboration structures, co-citation networks, keyword co-occurrence patterns, citation bursts, and influential references in order to identify the main paradigm shifts in the field. The results reveal a marked transition from descriptive morphology-centered research toward molecular systematics, DNA barcoding, phylogenomics, host-plant interactions, genomics, and integrative biodiversity analysis, particularly after 2010. Cluster and burst analyses further indicate that recent studies increasingly rely on genetic datasets, computational workflows, and multi-source evidence integration, while themes such as new species, genotype, chemical defence strategies, host plant, and mitochondrial genome have emerged as major focal points in different periods. Collaboration networks show that scientific production remains concentrated in several core clusters, although the growing contributions of China, Türkiye, and France reflect an expanding international research landscape. Beyond documenting the intellectual structure of Symphyta research, this study proposes a transferable bibliometric framework for tracking transformation in data-intensive biological disciplines. The findings may support future research planning, interdisciplinary collaboration, and methodological alignment in molecular and genetic research, biodiversity informatics, and related engineering-oriented analytical applications.

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