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Review

Overview of Genetic and Genomic Research Related to Stingless Bees (Meliponini): An AI-Assisted Science Mapping and Structural Topic Modeling Analysis

by
Larissa de Oliveira Rosa Marques
1,
Jamira Dias Rocha
1,
Leonardo Carlos Jeronimo Corvalán
1,
Júllia Costa dos Reis
2,
Cíntia Pelegrineti Targueta
2,
Pedro Vale de Azevedo Brito
3,
Carlos de Melo e Silva Neto
4,5,
Thiago Mafra Batista
6,
Mariana Pires de Campos Telles
2,7,
Renata de Oliveira Dias
2 and
Rhewter Nunes
1,*
1
Bioinformatics and Biodiversity Laboratory (LBB), Academic Institute of Health and Biological Sciences, State University of Goiás–UnU of Iporá, Iporá 76200-000, Brazil
2
Laboratory of Genetics & Biodiversity (LGBio), Institute of Biological Sciences, Federal University of Goiás, Goiânia 74001-970, Brazil
3
Department of Histology, Embryology and Cell Biology–Federal University of Goiás, Goiânia 74690-900, Brazil
4
Center of Excellence in Research and Innovation, Federal Institute of Goiás, Goiânia 74055-110, Brazil
5
Academic Institute of Agricultural Sciences and Sustainability, State University of Goiás, Ipameri 75780-000, Brazil
6
National Institute of the Atlantic Forest, Santa Teresa 29650-000, Brazil
7
School of Medical and Life Sciences, Pontifical Catholic University of Goiás, Goiânia 74605-010, Brazil
*
Author to whom correspondence should be addressed.
DNA 2026, 6(3), 42; https://doi.org/10.3390/dna6030042
Submission received: 11 May 2026 / Revised: 18 August 2026 / Accepted: 20 August 2026 / Published: 26 August 2026
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Abstract

Background/Objectives: Stingless bees (tribe Meliponini) are the most species-rich group of eusocial bees and critical pollinators across tropical ecosystems. Over the past seven decades, a growing number of studies have addressed their genetics and genomics, but coverage of the tribe remains taxonomically and geographically uneven. Here, we present a systematic evidence map and bibliometric science-mapping synthesis of this literature. Methods: We searched Scopus and Web of Science, retained 410 peer-reviewed articles published between 1950 and 2026, and applied structural topic modeling (STM) to characterize the thematic, temporal, taxonomic, and biogeographic structure of the corpus. Results: STM with K = 10 topics identified ten research themes, ranging from classical marker-based genetics and cytogenetics to phylogenomics, mitochondrial genomics, microbiome, and functional genomics. The estimated prevalence of phylogenomics/taxonomy and mitogenomics increased most steeply in recent years, a publication pattern consistent with—although not proof of—a shift toward genome-scale comparative approaches. Topic prevalence differed across biogeographic regions and subtribes: Neotropical and Meliponina-dominated studies were concentrated in population genetics, cytogenetics, and gene expression, whereas Indo-Australasian and Hypotrigonina-associated studies showed higher relative representation of DNA barcoding, mitogenomics, and microbiome research. Taxonomic representation was strongly skewed toward a few genera, with Melipona alone accounting for 43% of the corpus and most lineages across the Meliponini phylogeny remaining poorly studied. Conclusions: The principal contribution is a reproducible quantitative map of publication patterns; proposed research and conservation priorities are evidence-informed interpretations rather than direct outputs of STM.

1. Introduction

Stingless bees (Hymenoptera: Apidae: Meliponini) comprise approximately 605 described species distributed across tropical and subtropical regions of the Americas, Africa, Asia, and Oceania [1]. As the most species-rich group of eusocial bees, Meliponini exhibits remarkable ecological, behavioral, and morphological diversity, occupying a wide range of nesting habitats and foraging niches [1,2]. Their role as primary pollinators in tropical ecosystems is well documented, with numerous studies demonstrating their contribution to the reproductive success of both wild plant communities and economically important crops [3,4,5]. Beyond their ecological significance, stingless bees have been managed for honey and other hive products by indigenous and local communities for millennia, a practice known as meliponiculture that continues to expand in contemporary agroforestry and conservation programs [6].
The evolutionary history of Meliponini is characterized by deep biogeographic structure and complex patterns of diversification [7,8,9]. Recent phylogenomic analyses have revealed three major lineages corresponding to distinct biogeographic domains, with many genera showing non-monophyletic relationships or comprising independent evolutionary lineages [1]. This phylogenetic complexity is compounded by the frequent occurrence of cryptic species, morphologically similar taxa that are genetically distinct and often exhibit strong local adaptations. Such cryptic diversity has profound implications for taxonomy, conservation planning, and the sustainable management of meliponiculture stocks, as failure to recognize distinct evolutionary lineages can lead to inappropriate colony translocations and erosion of locally adapted gene pools [2,7,10].
Genetic and genomic research on stingless bees has evolved substantially over the past seven decades. Early studies relied on morphometric and cytogenetic approaches to delimit species and characterize karyotypic variation [11,12]. The advent of molecular markers in the 1990s, particularly microsatellites, amplified fragment length polymorphisms (AFLP), and mitochondrial DNA sequences, enabled population genetic analyses that revealed high levels of intracolonial polymorphism, pronounced population structuring, and complex patterns of gene flow [11,13,14,15,16]. More recently, the application of high-throughput sequencing technologies has facilitated phylogenomic reconstructions using ultraconserved elements (UCEs) and whole-genome data, as well as the assembly of complete mitochondrial genomes that have exposed unexpected patterns of gene rearrangement and heteroplasmy [17,18,19,20]. Concurrently, the availability of chromosome-level nuclear genome assemblies for model species such as Melipona quadrifasciata has opened new avenues for comparative genomics, functional gene family analyses, and investigations of the molecular basis of social behavior [21,22,23,24].
Previous syntheses have addressed important parts of this literature. They systematically reviewed the use and applicability of molecular markers in stingless bees [25], and synthesized Meliponini classification and biology [1]. Other reviews have focused on meliponiculture, conservation genetics, threats, or particular taxa. These contributions establish the biological background, but they do not quantitatively map how genetic and genomic themes vary simultaneously through time, across taxa, and among biogeographic regions in a reproducible corpus-level framework. The unresolved gap is therefore one of science mapping rather than a lack of narrative biological reviews.
Bibliometric and science-mapping approaches can synthesize large and heterogeneous bodies of literature, identify thematic trends, and reveal structural biases in research effort [26,27]. Structural topic modeling (STM), a probabilistic text-mining method, represents each document as a mixture of latent topics and estimates how the expected proportion of text assigned to each topic varies with document-level covariates [28,29]. Accordingly, STM demonstrates patterns in the published record—not changes in biological processes, the quality of individual studies, or causal drivers of research activity. This distinction guides our interpretation throughout the Discussion.
In this study, we present an artificial intelligence-assisted systematic evidence map and bibliometric science-mapping synthesis of the genetic and genomic literature on stingless bees. We compiled a corpus of 410 peer-reviewed articles published between 1950 and April 2026, extracted from Scopus and Web of Science, and applied STM to characterize the thematic, temporal, taxonomic, and biogeographic structure of this literature. Our objectives were to: (1) identify and describe the major research themes structuring the publication record; (2) quantify temporal changes in estimated topic prevalence and evaluate whether they are consistent with the hypothesized shift from marker-based to genome-scale approaches; (3) quantify taxonomic and biogeographic representation across topics; and (4) use the resulting evidence map, together with previous biological literature, to identify testable knowledge gaps. The novelty lies primarily in the integrated quantitative mapping—rather than in presenting the underlying biological observations as new—and in the reproducible linkage of topic trends to taxonomic and biogeographic coverage.

2. Materials and Methods

2.1. Search Strategy and Eligibility Criteria

We conducted systematic searches in Scopus and Web of Science (WoS) in April 2026 to identify peer-reviewed articles addressing genetic and genomic research on stingless bees (tribe Meliponini). All scripts used in this paper were documented and can be accessed on: https://github.com/Rhewter/Marques_et_al_Overview-of-genetic-and-genomic-research-related-to-stingless-bees. We structured the evidence map according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Ecology and Evolutionary biology (PRISMA-EcoEvo) reporting framework, where applicable [30]. Because we aimed to map thematic, taxonomic, and biogeographic patterns in the genetic and genomic literature on Meliponini rather than estimate a pooled effect size, the study is presented as a systematic evidence map and bibliometric synthesis, not as a meta-analysis or a classical systematic review.
We retrieved bibliometric data using custom R scripts built with the readxl, httr2, jsonlite, and rscopus packages. Search terms comprised the names of all extant and extinct Meliponini genera [1,18], joined by the Boolean operator OR. Eligible records were original peer-reviewed articles that used genetic or genomic methods to investigate Meliponini or bee-associated samples when the molecular analysis directly addressed stingless bee biology. We excluded non-article items (e.g., book chapters, conference abstracts, and errata) and molecular studies whose focal biological question concerned another organism or product rather than Meliponini (e.g., characterization of an isolated bacterial lineage without a direct question about the bee or its associated system).

2.2. Structural Topic Modeling

We built the text representation for STM using the stm R package 4.5.2, using titles, abstracts, author keywords, and indexed keywords from the articles [28,29,31]. To reduce taxonomic and geographic bias in the recovered topics which could otherwise merely reflect the most studied taxa and localities rather than methodological or conceptual themes, we removed: (i) stingless bee genus and species names, subgenus names and specific epithets from the species list of Lepeco et al. 2026 [7]; (ii) general English stopwords; (iii) general domain terms including bee, bees, stingless, meliponini, species, genus and genera; and (iv) locality terms and lexical residuals compiled in a custom stopword list. STM models were fitted with publication year as the primary prevalence covariate, modeled as a smooth spline: prevalence ~ s (year).
We evaluated K = 5, 8, 10, 12, 15, 20, 25, and 30 topics and compared semantic coherence, exclusivity, held-out likelihood, residual dispersion, and lower-bound diagnostics across solutions. The final model used K = 10 because it provided the best direct balance between semantic coherence and exclusivity while retaining interpretable, non-fragmented research themes. K = 5 had better semantic coherence but aggregated too many conceptually distinct themes. K = 20 had the highest exclusivity but fragmented the literature into excessively narrow subtopics. K = 15 had the highest composite score in our evaluation, but the difference relative to K = 10 was small, and the 10-topic solution was more parsimonious and better aligned with the biological objectives of the article (see Supplementary Diagnostic Figure S1).

2.3. Taxonomic, Biogeographic, and Subtribe Covariates

AI-assisted screening and taxon assignment were implemented through the OpenAI Responses API using GPT-5.2 and strict JSON-schema outputs. Eligibility screening followed two stages. Stage 1 classified every record from the title alone as yes, no, or unclear, with confidence and a manual-check flag. Stage 2 supplied the title and abstract for lower-confidence or ambiguous records and allowed the preliminary decision to be confirmed or changed. The prompt treated DNA barcoding, mitochondrial or nuclear sequencing, phylogenetics, population genetics, molecular markers, transcriptomics/gene expression, metagenomics/metabarcoding, and microbiome analyses as eligible when their relationship to stingless bees was explicit; non-molecular chemistry, behavior, ecology, morphology, product, toxicology, or management studies were excluded. Molecular analyses of pollen, microbes, plants, pathogens, nest material, or hive products were eligible only when the sampled material was explicitly associated with a stingless bee and the question addressed the bee-associated system. Records remaining ambiguous were flagged for manual assessment.
Taxon assignment used a separate two-step procedure. Candidate genus and species names were first detected by exact/normalized string matching against the Meliponini checklist in titles, abstracts, author keywords, and indexed keywords. GPT-5.2 then classified each candidate article–taxon pair as focal studied, included studied, contextual mention, not about the taxon, or unclear. A taxon counted as studied only when it was sampled, sequenced, genetically or cytogenetically analyzed, included in a phylogeny/barcoding/population-genetic analysis, or explicitly used as a study organism; background examples, taxonomic lists, database references, and unsupported keyword mentions were not counted. For a genus, studying one or more constituent species qualified the genus as studied. The model returned confidence, a concise rationale, and evidence grounded in the supplied title, abstract, and keywords. Articles studying multiple taxa were counted once per studied taxon, so taxon totals are not mutually exclusive.
All AI outputs were reviewed against the supplied bibliographic text by the author team; disagreements between the model output and human judgment were resolved in favor of the human decision, and corrected classifications were used in all downstream analyses. The model was not treated as an independent reviewer, and no formal inter-rater agreement or repeated-run stability statistic was calculated; this is a limitation of the screening procedure. Reproducibility is instead supported by the fixed decision rules, strict output schemas, versioned scripts, and release of the human-audited article–taxon classifications and derived corpus in the public repository. Because provider models can change, the final analysis starts from these archived audited outputs rather than requiring a new API run. The complete prompts and decision categories are reproduced in Supplementary Methods S1.

2.4. Phylogenetic Mapping

The genus-level phylogeny was derived from the Meliponini time tree of Lepeco et al. (2026) [7], pruned to a one-tip-per-genus representation. Genera that were not monophyletic in the original tree were excluded to avoid generating spurious phylogenetic inferences. Of the 44 genera present in the source tree, 40 were retained and four were excluded on this criterion (Frieseomelitta, Geniotrigona, Lepidotrigona and Plebeia); three of these four are among the ten most studied genera, so they appear in Figure 3 but not in Figure 4. The pruned tree was converted to an ultra-metric topology using the Grafen branch-length method. Article counts per genus were log1p-transformed for color scaling to reduce the visual dominance of Melipona. The resulting phylogenetic heatmap shows both branch colors (derived from the mean of descendant tip values) and tip-point colors (per-genus counts), with lateral markers indicating subtribe membership.

3. Results

3.1. Corpus Overview and Thematic Structure of Topics

The Scopus search returned 4961 raw records. After internal deduplication, 4951 records were screened. The final curated Scopus corpus retained 383 articles. The Web of Science search imported a total of 4400 raw records. After internal deduplication, 4396 records remained, of which 779 were unique relative to Scopus. These 779 Web of Science-exclusive records were screened with an AI-assisted title and abstract procedure, followed by manual checking. Thirty-one records were classified as relevant. Four of these were subsequently excluded as non-article items (e.g., book chapters, conference abstracts), leaving 27 Web of Science-exclusive articles. The final merged corpus comprised 410 articles (383 from Scopus plus 27 Web of Science-exclusive articles), published between 1950 and April 2026, with zero additional duplicates after merging. The STM with K = 10 identified ten topics that together capture the main research themes in Meliponini genetics and genomics (Table 1). Topics were labeled based on their highest-probability terms (prob, frex, lift, and score term weightings) and biological interpretation.

3.2. Temporal Trends in Research Themes

The strongest recent increases in topic prevalence were observed for phylogenomics/taxonomy (T1) and mitogenomics (T4), which had the steepest positive slopes over the final five years of the time series (2022–2026 window; Table 2; Figure 1). Estimated prevalence for T1 in 2026 was 0.226, and for T4 was 0.203, representing the two highest projected values among all topics. Topics T6 (mtDNA/COI markers) and T8 (gene expression/caste) also showed positive but more modest recent slopes (Table 2). High recent prevalence, though not necessarily with the steepest positive slope, was also observed for symbiotic bacteria/probiotics (T2), microbiome/exposure responses (T9), and pathogen/metabarcoding studies (T3). The incomplete 2026 data partly attenuate the estimated slopes for these more recently consolidated themes.
Together, these temporal estimates show that genome-scale topics occupy a larger share of the recent publication record, while several marker-based topics have flatter recent trajectories. This is consistent with a gradual change in research emphasis, but STM topic prevalence alone does not demonstrate technological replacement, changes in study quality, or a causal transition in the field. Marker-based, cytogenetic, functional-genomic, and microbiome studies continue to coexist in the corpus.

3.3. Biogeographic and Subtribe Structure

Topic prevalence differed markedly across biogeographic regions and subtribes (Figure 2). The comparison below is restricted to the 384 single-region articles, and for each region we report the four topics with the highest model-based mean prevalence. Neotropical studies (n = 313, 81.5% of single-region articles) showed the highest prevalence of population genetics (T7), cytogenetics/karyotypes (T5), gene expression/caste (T8), and allozyme/RAPD markers (T10). Indo-Australasian studies (n = 63, 16.4%) showed the highest prevalence of mtDNA/COI markers (T6), symbiotic bacteria/probiotics (T2), mitogenomics (T4), and population genetics (T7); relative to the corpus-wide mean, the largest positive differences for this region were for symbiotic bacteria/probiotics (T2) and mitogenomics (T4). Afrotropical studies (n = 8, 2.1%) showed the highest prevalence of mtDNA/COI markers (T6) and pathogens/metabarcoding (T3), but the sample size is too small for robust inference and should be treated as indicative only. Multi-region articles (n = 11) and unassigned articles (n = 15) were excluded from the main biogeographic analysis but are reported for completeness.
Subtribe patterns were broadly consistent with biogeographic patterns. Meliponina-associated articles (n = 313) showed higher prevalence of population genetics (T7), cytogenetics/karyotypes (T5), expression/caste (T8), and classical markers (T10). Hypotrigonina-associated articles (n = 74) showed higher prevalence of mtDNA/COI markers (T6), symbiotic bacteria/probiotics (T2), mitogenomics (T4), and population genetics (T7). Multi-subtribe articles (n = 11) and unassigned articles (n = 12) were excluded from the main subtribal analysis.
These patterns reflect strong taxonomic-geographic co-structure: most Neotropical articles study Meliponina, whereas a substantial proportion of Indo-Australasian and Afrotropical articles involve Hypotrigonina. These differences should be interpreted as the structure of literature rather than intrinsic biological differences between regions or subtribes.

3.4. Taxonomic Coverage and Bias

Taxonomic representation was highly uneven. The ten most studied genera, based on AI classification of taxa actually studied (not merely mentioned), were Melipona (178 articles), Tetragonula (46), Tetragonisca (44), Scaptotrigona (42), Partamona (40), Trigona (31), Heterotrigona (30), Frieseomelitta (20), Plebeia (19), and Lepidotrigona (18) (Figure 3). Among the 10 most studied species, 7 belong to the neotropical clade. Melipona alone accounted for 43% of the corpus.
Exploratory topic profiles by genus (Figure 3) revealed that Melipona was most strongly associated with expression/caste (T8), population genetics (T7), and cytogenetics/karyotypes (T5); Tetragonula with mtDNA/COI markers (T6), mitogenomics (T4), and microbiome/exposure (T9); Tetragonisca with population genetics (T7), allozyme/RAPD markers (T10), and cytogenetics/karyotypes (T5); Scaptotrigona with population genetics (T7), expression/caste (T8), and pathogens/metabarcoding (T3); Partamona with cytogenetics/karyotypes (T5), population genetics (T7), and allozyme/RAPD markers (T10); Heterotrigona with symbiotic bacteria/probiotics (T2), mtDNA/COI markers (T6), and mitogenomics (T4); and Lepidotrigona with mitogenomics (T4), mtDNA/COI markers (T6), and phylogenomics/taxonomy (T1). These associations are exploratory and reflect the available literature; they may be influenced by a few studies in less-studied genera.
Mapping article counts onto the genus-level phylogeny (Figure 4) showed that research effort is concentrated in a subset of lineages distributed non-randomly across the Meliponini phylogeny. Several genera had zero or near-zero articles. Because four genera that were not monophyletic in the source tree were excluded from the pruned topology (Section 2.4), Frieseomelitta, Plebeia and Lepidotrigona are absent from Figure 4 despite ranking among the ten most studied genera; the taxonomic ranking in Figure 3 is therefore the complete one. This phylogenetic bias reinforces the conclusion that large portions of Meliponini diversity remain poorly studied from a genetic and genomic perspective.

4. Discussion

4.1. The Evolution of Genetic and Genomic Research on Stingless Bees

Our STM results quantify changes in the thematic composition of the publication record over seven decades. Specifically, phylogenomics/taxonomy (T1) and mitogenomics (T4) had the steepest estimated recent slopes, whereas population genetics (T7), cytogenetics (T5), and allozyme/RAPD markers (T10) had flatter recent trajectories. These estimates support the narrower conclusion that genome-scale themes have gained relative prominence in recent publications. They do not establish that marker-based research has been replaced, that the field has completed a technological transition, or why publication priorities changed. The broader biological interpretation—that falling sequencing costs and expanded genomic resources enabled new questions—is consistent with molecular ecology literature [32,33] but is not tested by our STM.
Previous biological studies explain the content represented by these topics. Microsatellites have been widely used to assess genetic diversity, population structure, and gene flow because of their polymorphism and codominant inheritance [11,13,14]. More recent UCE, whole-genome, and mitogenome studies have increased phylogenetic resolution and documented mitochondrial rearrangement and heteroplasmy [18,19,23,34,35]. These are findings of the primary literature, not discoveries generated by the topic model. Our contribution is to estimate when and where these themes appear in the corpus and to show that their representation is uneven across taxa and regions.
The science map also places limits on biological generalization. Expression/caste (T8) and other functional themes remain concentrated in a restricted taxonomic subset [36], while many genera have few or no articles. Consequently, hypotheses about the generality of caste regulation, genome evolution, or population processes across Meliponini remain under-tested. The statement that broader genomic sampling would improve comparative inference is an evidence-informed recommendation based on this coverage gap and on previous biological literature [1,18,32]; it is not a direct estimate produced by STM.

4.2. Taxonomic and Geographic Biases: Causes and Consequences

The taxonomic and geographic biases quantified here concern the distribution of publications. Melipona accounts for 43% of the corpus, and Neotropical/Meliponina-associated studies predominate. Previous literature suggests plausible practical, economic, and historical explanations, including the manageability and cultural importance of Melipona and the concentration of established research programs in Brazil and Mexico [37,38,39]. These explanations were not included as covariates and therefore remain interpretations rather than causes demonstrated by our analysis.
However, the majority of stingless bee genera remain poorly studied. Of the 45 extant genera in the checklist used here, six (Asperplebeia, Duckeola, Lisotrigona, Meliwillea, Nogueirapis and Plebeiella) are not represented at all in the genetic and genomic corpus; a further six genera in the checklist are known only from fossils, for which the absence of genetic data is expected rather than a research gap. New genera continue to be described [1,18,40], so this count is a snapshot of current coverage. This taxonomic gap limits our ability to draw general conclusions about the evolutionary processes shaping stingless bee diversity [41]. For example, patterns of population structure, gene flow, and local adaptation documented in Melipona may not be representative of other genera with different life histories, dispersal abilities, or ecological requirements. Similarly, the molecular mechanisms underlying social behavior, caste determination, and division of labor may vary across lineages, and insights gained from model species may not be directly transferable to understudied taxa.
The relative scarcity of Afrotropical and some Indo-Australasian studies is likewise a publication-coverage result, not evidence that those faunas are biologically less diverse or less important. The distinct topic profiles partly reflect taxonomic–geographic co-structure, and the Afrotropical estimate is especially uncertain because it is based on only eight single-region articles. Suggestions for international collaboration, capacity building, and infrastructure investment follow from prior policy and biological literature [18,42]; they should be evaluated as recommendations, not STM outputs.
Conservation implications require the same distinction. The evidence map shows that genetic and genomic evidence is concentrated in a small subset of taxa and regions. Previous biological literature documents threats to stingless bees and demonstrates why genetic diversity and population structure can matter for management [43]. We therefore infer that transferring conclusions from heavily studied taxa to poorly sampled lineages may be risky. Which taxa or regions should receive conservation action, however, requires additional data on threat, abundance, evolutionary distinctiveness, feasibility, and local priorities; publication scarcity alone is not a conservation-priority score.

4.3. Conservation Genetics and the Risks of Human-Mediated Gene Flow

The STM did not directly test the effects of human-mediated gene flow. Rather, it identified population genetics (T7) as a prominent theme, especially in the Neotropical/Meliponina-associated literature. The management implications discussed here come from primary conservation-genetic studies, which have reported population structure and differentiation among regions [2,10,44,45], not from topic prevalence itself. These studies make colony translocation a biologically plausible management concern, but the magnitude and direction of risk remain taxon- and context-dependent.
For example, studies of managed Heterotrigona itama and Geniotrigona thoracica in Southeast Asia reported contrasting nuclear and mitochondrial patterns associated with colony transport, nest division, trade, and dispersal [46,47]. These cases illustrate potential mechanisms and justify local genetic assessment. They do not support a universal restriction rule for all stingless bees; management guidance should be based on taxon-specific evidence, provenance, disease risk, and applicable regulation.
The conservation genetics literature on stingless bees also highlights the importance of preserving regional genetic diversity. Multiple studies have demonstrated that populations from different regions often represent distinct evolutionary lineages, even within morphologically similar species [2,10,45]. For example, morphometric and molecular analyses of Scaptotrigona hellwegeri populations from different regions revealed marked differentiation, suggesting that these populations should be treated as separate genetic lineages for conservation planning [10]. Similarly, studies of Melipona yucatanica documented morphometric and molecular differentiation between Mexican and Guatemalan populations, suggesting that they may represent distinct species and emphasizing the need for taxonomic reevaluation at regional scales [45].
Accordingly, fine-scale genetic data can inform the delineation of management units and evaluation of colony movements [14,47,48,49,50], but our bibliometric analysis cannot prescribe conservation programs or regulation. We present these applications as implications of the biological literature and as questions made visible by the mapped evidence gaps.
At a broader phylogenetic scale, provides the molecular framework used to define groups in our analysis [18]. Our mapping shows how unevenly the literature covers that framework; it does not independently test the phylogeny or infer biological differences among its branches.

4.4. Mitogenomics, Gene Rearrangement and the Limits of DNA Barcoding

The STM result relevant to this section is that mitogenomics (T4) has one of the steepest recent increases in estimated prevalence, while mtDNA/COI markers and barcoding form a distinct topic (T6). This separation shows that the corpus increasingly discusses complete mitochondrial genomes and genome architecture rather than only short-marker applications. It does not itself demonstrate gene rearrangement, heteroplasmy, or barcode failure. Those biological phenomena are documented in primary studies of Lepidotrigona, Tetragonula, Melipona, and other taxa [23,35,51,52].
The primary literature raises lineage-specific limitations of mitochondrial inference. Rearrangement, heteroplasmy, introgression, or heterogeneous substitution rates can complicate species delimitation and phylogenetic reconstruction based on COI alone [23,51,52,53,54]. Alternative mitochondrial loci and integration with nuclear data may improve some analyses, but their performance requires empirical validation across taxa. Our science map therefore identifies an increasing and taxonomically uneven mitogenomic literature; the recommendation to compare markers and integrate nuclear evidence is a literature-based interpretation, not an STM-derived test of barcode accuracy.
Similarly, the possibility that heteroplasmy can affect phylogeographic inference follows from mitochondrial biology and the cited case studies [23], not from topic prevalence. Future work could test how often these complications alter conclusions by comparing mitochondrial and nuclear estimates across multiple lineages. This is a testable research direction suggested by the synthesis rather than a demonstrated tribe-wide pattern.

4.5. Future Directions: Toward a Comparative Genomics Framework

The recent rise in phylogenomics/taxonomy (T1) and mitogenomics (T4) is consistent with increasing use of genome-scale approaches, but it does not demonstrate wholesale replacement of marker-based studies. The clearest map-based gap is uneven coverage: genome-scale themes and article counts are concentrated in a limited set of lineages and regions. On that basis, broader comparative sampling is a reasonable research opportunity, not a priority ranking produced automatically by STM.
Previous biological literature shows that high-quality reference genomes and broader phylogenetic sampling can enable comparisons of gene-family evolution, genome structure, and phenotype [1,18,19]. The statement that only 14 stingless bee species had assemblies in the databases consulted should be read as a time-stamped resource inventory, not an STM result. Sampling Afrotropical, Indo-Australasian, and understudied Neotropical lineages [55] could reduce the coverage imbalance identified here, but choices among taxa should also consider specimen availability, assembly feasibility, biological questions, local partnerships, and community priorities.
Genome-wide association, landscape genomics, and integration with ecological or environmental data are promising approaches described in the wider literature [56,57]. Our analysis does not evaluate their effectiveness in Meliponini. We therefore present them as candidate directions that could address questions exposed by the evidence map, particularly the limited ability to generalize beyond a few taxa, rather than as necessary outcomes of the observed publication trends.
Applied claims likewise require direct evaluation. Genomic resources may support monitoring, breeding, disease research, or management [46,58], but benefits, risks, and accessibility will depend on validation in each system and on collaboration with local and Indigenous communities. These proposals are expert interpretations informed by the mapped gaps and cited literature; they are not direct consequences of the STM estimates.

5. Conclusions

This systematic evidence map and bibliometric synthesis quantify the thematic, temporal, taxonomic, and biogeographic structure of genetic and genomic publications on stingless bees. STM identified ten research themes. Phylogenomics/taxonomy and mitogenomics showed the steepest recent increases in estimated topic prevalence, a publication pattern consistent with growing use of genome-scale approaches. STM does not establish technological replacement, causal change, or biological processes; those interpretations require evidence beyond topic prevalence.
The principal contribution is the integrated quantitative mapping of a 410-article corpus. The analysis confirms and measures previously recognized asymmetries: Melipona accounts for 43% of the corpus, Neotropical research predominates, and many genera have little or no representation. These are gaps in the published evidence base and limits on generalization, not direct measures of biological diversity, conservation need, or research quality.
The map can guide hypothesis generation and transparent sampling decisions. Broader genomic and functional sampling of underrepresented lineages may improve comparative inference, but priorities should be set together with biological importance, threat, feasibility, local expertise, and stakeholder needs. Likewise, conservation and meliponiculture recommendations must be tested with taxon- and context-specific genetic, ecological, and socioeconomic evidence.
In summary, this study contributes a reproducible science map rather than a claim that the underlying biological patterns are themselves novel. It distinguishes what STM demonstrates—relative thematic prevalence and its associations with year, taxon, and region—from what primary studies demonstrate biologically and from prospective directions that remain hypotheses or expert recommendations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/dna6030042/s1, Figure S1: Diagnostics for selecting the best number of topics in a structural topic analysis on genetics and genomics research related to Meliponini bees. Final K: 10 and the best composite-score K: 15; Figure S2: Estimated topic prevalence over time in a structural topic analysis on genetics and genomics research related to Meliponini bees. Highlighted lines indicate topics with the strongest positive recent slope; Methods S1: AI-assisted screening and taxon-assignment prompts.

Author Contributions

Conceptualization, R.N.; methodology, L.d.O.R.M., J.D.R. and R.N.; software, L.d.O.R.M., J.D.R., L.C.J.C., J.C.d.R., R.d.O.D. and R.N.; validation, L.d.O.R.M., J.D.R. and R.N.; formal analysis, L.d.O.R.M., J.D.R. and R.N.; data curation, L.d.O.R.M., J.D.R. and R.N.; writing—original draft preparation, L.d.O.R.M., J.D.R. and R.N.; writing—review and editing, L.d.O.R.M., J.D.R., L.C.J.C., J.C.d.R., C.P.T., P.V.d.A.B., C.d.M.e.S.N., T.M.B., M.P.d.C.T., R.d.O.D. and R.N.; visualization, L.d.O.R.M., J.D.R. and R.N.; supervision, R.d.O.D. and R.N.; project administration, R.N.; funding acquisition, M.P.d.C.T. and R.N. All authors have read and agreed to the published version of the manuscript.

Funding

The work is associated with the Instituto Nacional de Ciência e Tecnologia em Ecologia, Evolução e Conservação da Biodiversidade (INCT-EECBio), funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (#465610/2014-5 and #409197/2024-6) and Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG) (#201810267000023); and Rede Biogenomas: Chamada CNPq/MCTI/FNDCT N 22/2024, Processo: 444522/2024-7. This work was also supported by the Center for Research in Biodiversity and Ecosystem Services (CPBioS), funded by FAPEG (#202510267001860), CEGGen—Convênio PD&I N°03/2025—FAPEG #202410267001291, “PELD Araguaia” (CNPq #445733/2024-1/FAPEG #202510267001637), “Rede HidroCerrado” (FAPEG #202410267000982), “Centro de Excelência em Segurança Hídrica do Cerrado—CEHIDRA” (FAPEG #202400020017042), PPBio Araguaia project (CNPq #441114/2023-7) and to Chamada de Apoio à Pesquisa (FAPEG #202610267001232). JDR was funded by PDCTR postdoctoral scholarship (CNPq: #316117/2025-0 and FAPEG: #20251026700116). LCJC received a scholarship from FAPEG. CdMeSN and MPdCT thanks CNPq for the continuation of the research productivity fellowship.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data and scripts used in this article are all available on https://github.com/Rhewter/Marques_et_al_Overview-of-genetic-and-genomic-research-related-to-stingless-bees.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Estimated STM topic prevalence over publication year for the topics with the strongest positive slopes during 2022–2026. For each article, STM estimates a mixture of topics whose proportions sum to 1; the curve is the model-based expected proportion assigned to a topic at each year under the prevalence model prevalence ~ s (year), with the shaded band showing the 95% confidence interval. Curves therefore describe changes in the relative thematic composition of the corpus, not numbers of papers, causal changes, or replacement of one method by another. The partial 2026 publication year should be interpreted cautiously.
Figure 1. Estimated STM topic prevalence over publication year for the topics with the strongest positive slopes during 2022–2026. For each article, STM estimates a mixture of topics whose proportions sum to 1; the curve is the model-based expected proportion assigned to a topic at each year under the prevalence model prevalence ~ s (year), with the shaded band showing the 95% confidence interval. Curves therefore describe changes in the relative thematic composition of the corpus, not numbers of papers, causal changes, or replacement of one method by another. The partial 2026 publication year should be interpreted cautiously.
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Figure 2. Model-based mean topic prevalence by biogeographic region. Prevalence is the expected proportion of an article’s text mixture assigned to a topic; values are averaged over articles assigned to each region in the K = 10 region-covariate STM. Topic proportions sum to 1 within each article, so values show relative thematic emphasis rather than article counts or biological trait frequencies. Region denotes the known distribution of the studied genus, not necessarily the sampling locality. Only single-region articles are shown in the main comparison; Afrotropical estimates (n = 8) are descriptive because of the small sample.
Figure 2. Model-based mean topic prevalence by biogeographic region. Prevalence is the expected proportion of an article’s text mixture assigned to a topic; values are averaged over articles assigned to each region in the K = 10 region-covariate STM. Topic proportions sum to 1 within each article, so values show relative thematic emphasis rather than article counts or biological trait frequencies. Region denotes the known distribution of the studied genus, not necessarily the sampling locality. Only single-region articles are shown in the main comparison; Afrotropical estimates (n = 8) are descriptive because of the small sample.
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Figure 3. Mean document-level STM topic proportions for the ten most studied genera in the Meliponini genetics and genomics corpus. For each article, the ten topic proportions sum to 1; for each genus, these proportions were averaged across articles classified as actually studying that genus. Thus, a larger value means that a greater share of the text in that genus-specific subset was assigned to the topic; it does not represent the percentage of all articles in the field. Genera are ranked by AI-assisted, human-audited counts of articles studying each taxon. Because articles can study multiple genera, genus groups are not mutually exclusive, and profiles for less-studied genera are exploratory.
Figure 3. Mean document-level STM topic proportions for the ten most studied genera in the Meliponini genetics and genomics corpus. For each article, the ten topic proportions sum to 1; for each genus, these proportions were averaged across articles classified as actually studying that genus. Thus, a larger value means that a greater share of the text in that genus-specific subset was assigned to the topic; it does not represent the percentage of all articles in the field. Genera are ranked by AI-assisted, human-audited counts of articles studying each taxon. Because articles can study multiple genera, genus groups are not mutually exclusive, and profiles for less-studied genera are exploratory.
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Figure 4. Phylogenetic distribution of research effort by genus in the Meliponini genetics and genomics corpus. Tip points encode the AI-assisted, human-audited article count for each genus; branch colors encode the mean count among descendant tips and are a visualization aid, not an inferred ancestral state or phylogenetic comparative statistic. Counts were log1p-transformed only for the color scale. Lateral markers indicate subtribe membership. The genus-level topology [18] is reduced to one terminal per genus, retaining 40 of the 44 genera present in the original tree; the four genera that were not monophyletic in that tree (Frieseomelitta, Geniotrigona, Lepidotrigona, Plebeia) were excluded, so the figure understates coverage for those lineages. The figure maps uneven literature coverage and should not be interpreted as biological trait evolution.
Figure 4. Phylogenetic distribution of research effort by genus in the Meliponini genetics and genomics corpus. Tip points encode the AI-assisted, human-audited article count for each genus; branch colors encode the mean count among descendant tips and are a visualization aid, not an inferred ancestral state or phylogenetic comparative statistic. Counts were log1p-transformed only for the color scale. Lateral markers indicate subtribe membership. The genus-level topology [18] is reduced to one terminal per genus, retaining 40 of the 44 genera present in the original tree; the four genera that were not monophyletic in that tree (Frieseomelitta, Geniotrigona, Lepidotrigona, Plebeia) were excluded, so the figure understates coverage for those lineages. The figure maps uneven literature coverage and should not be interpreted as biological trait evolution.
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Table 1. Ten research themes identified by structural topic modeling (K = 10). Topics are labeled based on the highest-probability and most exclusive terms (FREX) and representative articles.
Table 1. Ten research themes identified by structural topic modeling (K = 10). Topics are labeled based on the highest-probability and most exclusive terms (FREX) and representative articles.
TopicLabelShort NameBroad Theme
1Phylogenomics, taxonomy, and evolutionary relationshipsPhylogenomics/taxonomyEvolutionary relationships and taxonomy
2Symbiotic bacteria and probioticsSymbiotic bacteria/probioticsMicrobiome and symbiosis
3Viral pathogens, floral resources, and metabarcodingPathogens/metabarcodingDisease ecology and environmental DNA
4Mitogenomics and mitochondrial genome evolutionMitogenomicsGenome architecture and comparative genomics
5Cytogenetics, karyotypes, and chromosome architectureCytogenetics/karyotypesCytogenetics and cytogenomics
6mtDNA/COI marker assays and DNA barcodingmtDNA/COI markersMarker assays, barcoding, and molecular identification
7Population genetics, microsatellites, and morphometricsPopulation geneticsPopulation structure and phenotypic differentiation
8Gene expression, caste differentiation, and reproductive geneticsExpression/casteFunctional and reproductive genomics
9Gut microbiome, detoxification, and exposure responsesMicrobiome/exposureMicrobiome and environmental stress
10Allozyme, RAPD, and enzyme polymorphism markersAllozyme/RAPD markersClassical marker-based genetics
Table 2. Topics with the strongest recent increase in prevalence (2022–2026 window). Mean recent prevalence is the average prevalence across 2022–2026; slope is the coefficient of a linear regression of prevalence on year within this window; prevalence in 2026 is the estimated prevalence in the final year.
Table 2. Topics with the strongest recent increase in prevalence (2022–2026 window). Mean recent prevalence is the average prevalence across 2022–2026; slope is the coefficient of a linear regression of prevalence on year within this window; prevalence in 2026 is the estimated prevalence in the final year.
RankTopicThemeMean Recent PrevalenceSlopePrevalence in 2026
1T1Phylogenomics/taxonomy0.1150.0350.226
2T4Mitogenomics0.1370.0340.203
3T6mtDNA/COI markers0.0920.0030.087
4T8Expression/caste0.0620.0020.064
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MDPI and ACS Style

de Oliveira Rosa Marques, L.; Rocha, J.D.; Corvalán, L.C.J.; dos Reis, J.C.; Targueta, C.P.; Brito, P.V.d.A.; Silva Neto, C.d.M.e.; Mafra Batista, T.; Telles, M.P.d.C.; Dias, R.d.O.; et al. Overview of Genetic and Genomic Research Related to Stingless Bees (Meliponini): An AI-Assisted Science Mapping and Structural Topic Modeling Analysis. DNA 2026, 6, 42. https://doi.org/10.3390/dna6030042

AMA Style

de Oliveira Rosa Marques L, Rocha JD, Corvalán LCJ, dos Reis JC, Targueta CP, Brito PVdA, Silva Neto CdMe, Mafra Batista T, Telles MPdC, Dias RdO, et al. Overview of Genetic and Genomic Research Related to Stingless Bees (Meliponini): An AI-Assisted Science Mapping and Structural Topic Modeling Analysis. DNA. 2026; 6(3):42. https://doi.org/10.3390/dna6030042

Chicago/Turabian Style

de Oliveira Rosa Marques, Larissa, Jamira Dias Rocha, Leonardo Carlos Jeronimo Corvalán, Júllia Costa dos Reis, Cíntia Pelegrineti Targueta, Pedro Vale de Azevedo Brito, Carlos de Melo e Silva Neto, Thiago Mafra Batista, Mariana Pires de Campos Telles, Renata de Oliveira Dias, and et al. 2026. "Overview of Genetic and Genomic Research Related to Stingless Bees (Meliponini): An AI-Assisted Science Mapping and Structural Topic Modeling Analysis" DNA 6, no. 3: 42. https://doi.org/10.3390/dna6030042

APA Style

de Oliveira Rosa Marques, L., Rocha, J. D., Corvalán, L. C. J., dos Reis, J. C., Targueta, C. P., Brito, P. V. d. A., Silva Neto, C. d. M. e., Mafra Batista, T., Telles, M. P. d. C., Dias, R. d. O., & Nunes, R. (2026). Overview of Genetic and Genomic Research Related to Stingless Bees (Meliponini): An AI-Assisted Science Mapping and Structural Topic Modeling Analysis. DNA, 6(3), 42. https://doi.org/10.3390/dna6030042

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