Ecological Epigenetics: Research Fronts, Taxonomic Gaps, and a Five-Year Forecast

Öz

Ecological epigenetics investigates the role of molecular modifications, including DNA methylation, in heritable phenotypic variation in natural populations, often arising in response to environmental conditions. Because such modifications can respond to environmental change within a single generation, the field has direct relevance for understanding adaptation, plasticity, and the persistence of populations under habitat alteration and climate change. However, bibliometric attention has focused predominantly on the biomedical side of epigenetics, particularly cancer and clinical applications, while the structure and trajectory of the ecological literature have not been mapped. In the present study, 962 records published between 2000 and April 2026 were identified in the Web of Science Core Collection after two-stage filtering and analysed with the bibliometrix R package, complemented by time-series forecasting and segmented-regression analysis. The literature expanded at a compound annual rate of 12.16%, with output decelerating after a structural break in approximately 2014. Plants dominated the corpus at 52.70%, whereas mammals, fish, birds, reptiles, amphibians, and the invertebrate groups remained under-represented relative to their ecological and conservation importance. Two-thirds of records could not be linked to a specific ecosystem, reflecting the field’s strong laboratory orientation. Keyword co-occurrence networks organised the field around three macro-themes: a molecular-mechanism axis, an ecological-context axis, and a conservation-and-management axis. Multiple growth models converged on a marked slowdown in publication growth, consistent with approaching saturation rather than continued exponential expansion, projecting a near-flat output of roughly 52–54 records per year through 2030 (95% bounds widening to 14–89) and a model-averaged carrying capacity near 1,835 cumulative records. The taxonomic imbalance documented here reveals a structural gap in the field’s coverage of biodiversity, with implications for conservation genetics and the design of future ecological-epigenetics research.

Anahtar Kelimeler

• Ecological epigenetics
• Bibliometric analysis
• Time-series forecast
• Ensemble forecast
• Publication saturation
• Taxonomic gaps

Etik Beyan

Ethics committee approval was not required, because this study involved no experiments on animals or humans.

Teşekkür

This research received no external funding. The author is grateful to Gizem Nazlı Ural for her valuable comments on the manuscript. Claude (Anthropic) was used to assist with grammar correction and language improvement, and the author reviewed and edited the output.

Kaynakça

1. Allis, C. D., & Jenuwein, T. (2016). The molecular hallmarks of epigenetic control. Nature Reviews Genetics, 17(8), 487–500. https://doi.org/10.1038/nrg.2016.59
2. Anastasiadi, D., Venney, C. J., Bernatchez, L., & Wellenreuther, M. (2021). Epigenetic inheritance and reproductive mode in plants and animals. Trends in Ecology & Evolution, 36(12), 1124–1140. https://doi.org/10.1016/j.tree.2021.08.006
3. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
4. Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521–530. https://doi.org/10.1016/S0169-2070(00)00066-2
5. Balard, A., Baltazar-Soares, M., Eizaguirre, C., & Heckwolf, M. J. (2024). An epigenetic toolbox for conservation biologists. Evolutionary Applications, 17(6), e13699. https://doi.org/10.1111/eva.13699
6. Baltazar-Soares, M., Balard, A., & Heckwolf, M. J. (2024). Epigenetic diversity and the evolutionary potential of wild populations. Evolutionary Applications, 17(10), e70011. https://doi.org/10.1111/eva.70011
7. Bates, D., Maechler, M., & Jagan, M. (2025). Matrix: Sparse and dense matrix classes and methods (R package version 1.7-4) [Computer software]. https://CRAN.R-project.org/package=Matrix
8. Berger, S. L., Kouzarides, T., Shiekhattar, R., & Shilatifard, A. (2009). An operational definition of epigenetics. Genes & Development, 23(7), 781–783. https://doi.org/10.1101/gad.1787609

9. Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70–83. https://doi.org/10.1016/j.csda.2017.11.003
10. Bird, A. (2007). Perceptions of epigenetics. Nature, 447(7143), 396–398. https://doi.org/10.1038/nature05913
11. Blondeau-Bidet, E., Banousse, G., L’Honoré, T., Farcy, E., Cosseau, C., & Lorin-Nebel, C. (2023). The role of salinity on genome-wide DNA methylation dynamics in European sea bass gills. Molecular Ecology, 32(18), 5089–5109. https://doi.org/10.1111/mec.17089
12. Bonduriansky, R., & Day, T. (2009). Nongenetic inheritance and its evolutionary implications. Annual Review of Ecology, Evolution, and Systematics, 40, 103–125. https://doi.org/10.1146/annurev.ecolsys.39.110707.173441
13. Bonnet, X., Shine, R., & Lourdais, O. (2002). Taxonomic chauvinism. Trends in Ecology & Evolution, 17(1), 1–3. https://doi.org/10.1016/S0169-5347(01)02381-3
14. Bornmann, L., Haunschild, R., & Mutz, R. (2021). Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications, 8, Article 224. https://doi.org/10.1057/s41599-021-00903-w
15. Bossdorf, O., Richards, C. L., & Pigliucci, M. (2008). Epigenetics for ecologists. Ecology Letters, 11(2), 106–115. https://doi.org/10.1111/j.1461-0248.2007.01130.x
16. Boyle, P., Clement, K., Gu, H., Smith, Z. D., Ziller, M., Fostel, J. L., Holmes, L., Meldrim, J., Kelley, F., Gnirke, A., & Meissner, A. (2012). Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biology, 13, Article R92. https://doi.org/10.1186/gb-2012-13-10-r92
17. Brunson, J. C. (2020). ggalluvial: Layered grammar for alluvial plots. Journal of Open Source Software, 5(49), 2017. https://doi.org/10.21105/joss.02017
18. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
19. Caizergues, A. E., Le Luyer, J., Grégoire, A., Szulkin, M., Senar, J.-C., Charmantier, A., & Perrier, C. (2022). Epigenetics and the city: Non-parallel DNA methylation modifications across pairs of urban-forest Great tit populations. Evolutionary Applications, 15(1), 149–165. https://doi.org/10.1111/eva.13334
20. Çakır, M., Oral, O., Ural, G. N., & Yılmaz, M. (2025). Mapping the research landscape of machine learning in aquaculture: A bibliometric analysis with forecasted output by 2030. Aquaculture, 612, Article 743258. https://doi.org/10.1016/j.aquaculture.2025.743258
21. Ching, T. (2024). qs2: Efficient serialization of R objects (R package version 0.2.0) [Computer software]. https://CRAN.R-project.org/package=qs2
22. Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661–703. https://doi.org/10.1137/070710111
23. Crameri, F., Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature Communications, 11, Article 5444. https://doi.org/10.1038/s41467-020-19160-7
24. Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
25. Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 74(1), 33–43. https://doi.org/10.1093/biomet/74.1.33
26. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.1080/01621459.1979.10482531
27. Donthu, N.,Kumar,S.,Mukherjee,D.,Pandey,N.,&Lim,W.M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
28. Ebadi, A., & Schiffauerova, A. (2015). On the relation between the small world structure and scientific activities. PLOS ONE, 10(3), Article e0121129. https://doi.org/10.1371/journal.pone.0121129
29. Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008). Forecasting and operational research: A review. Journal of the Operational Research Society, 59(9), 1150–1172. https://doi.org/10.1057/palgrave.jors.2602597
30. Gamer, M., Lemon, J., Fellows, I., & Singh, P. (2019). irr: Various coefficients of interrater reliability and agreement (R package version 0.84.1) [Computer software]. https://CRAN.R-project.org/package=irr
31. Garnier, S., Ross, N., Rudis, R., Camargo, A. P., Sciaini, M., & Scherer, C. (2024). viridis(Lite): Colorblind-friendly color maps for R (R package version 0.6.5) [Computer software]. https://CRAN.R-project.org/package=viridis
32. Goldberg, A. D., Allis, C. D., & Bernstein, E. (2007). Epigenetics: A landscape takes shape. Cell, 128(4), 635–638. https://doi.org/10.1016/j.cell.2007.02.006
33. Gunasekara, C. W. R., Rajapaksha, L. G. T. G., & De Alwis, P. S. (2026). Heavy metal-induced epigenetic disruption in aquatic organisms: A review of mechanisms, impacts, and implications for ecotoxicology. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 306, Article 110552. https://doi.org/10.1016/j.cbpc.2026.110552
34. Hackerott, S., Virdis, F., Flood, P. J., Garcia Souto, D., Paez, W., & Eirin-Lopez, J. M. (2023). Relationships between phenotypic plasticity and epigenetic variation in two Caribbean Acropora corals. Molecular Ecology, 32(17), 4814–4828. https://doi.org/10.1111/mec.17072
35. Heard, E., & Martienssen, R. A. (2014). Transgenerational epigenetic inheritance: Myths and mechanisms. Cell, 157(1), 95–109. https://doi.org/10.1016/j.cell.2014.02.045
36. Heraghty, S. D., Rahman, S. R., Verble, K. M., & Lozier, J. D. (2024). Examining the effects of environment, geography, and elevation on patterns of DNA methylation across populations of two widespread bumble bee species. Genome Biology and Evolution, 16(10), Article evae207. https://doi.org/10.1093/gbe/evae207
37. Hu, J., & Barrett, R. D. H. (2017). Epigenetics in natural animal populations. Journal of Evolutionary Biology, 30(9), 1612–1632. https://doi.org/10.1111/jeb.13130
38. Hu, Y., Zhou, W., & Wei, F. (2025). Conservation epigenomics: Integrating epigenetic mechanisms into species conservation. Science China Life Sciences, 68(9), 2800–2803. https://doi.org/10.1007/s11427-025-2945-4
39. Hukkanen, M., Hsu, B.-Y., Cossin-Sevrin, N., Crombecque, M., Delaunay, A., Hollmen, L., Kaukonen, R., Konki, M., Lund, R., Marciau, C., Stier, A., & Ruuskanen, S. (2023). From maternal glucocorticoid and thyroid hormones to epigenetic regulation of offspring gene expression: An experimental study in a wild bird species. Evolutionary Applications, 16(10), 1753–1769. https://doi.org/10.1111/eva.13598
40. Humphries, M. D., & Gurney, K. (2008). Network ‘small world ness’: A quantitative method for determining canonical network equivalence. PLOS ONE, 3(4), Article e0002051. https://doi.org/10.1371/journal.pone.0002051
41. Husby, A. (2022). Wild epigenetics: Insights from epigenetic studies on natural populations. Proceedings of the Royal Society B: Biological Sciences, 289(1968), Article 20211633. https://doi.org/10.1098/rspb.2021.1633
42. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
43. Hyndman, R. J., & Billah, B. (2003). Unmasking the Theta method. International Journal of Forecasting, 19(2), 287–290. https://doi.org/10.1016/S0169-2070(01)00143-1
44. Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
45. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
46. Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454. https://doi.org/10.1016/S0169-2070(01)00110-8
47. Jablonka, E., & Raz, G. (2009). Transgenerational epigenetic inheritance: Prevalence, mechanisms, and implications for the study of heredity and evolution The Quarterly Review of Biology, 84(2), 131–176. https://doi.org/10.1086/598822
48. Jaenisch, R., & Bird, A. (2003). Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals. Nature Genetics, 33(Suppl), 245–254. https://doi.org/10.1038/ng1089
49. Jiang, C., Hu, Y., Wang, S., & Chen, C. (2023). Emerging trends in DNA and RNA methylation modifications in type 2 diabetes mellitus: A bibliometric and visual analysis from 1992 to 2022. Frontiers in Endocrinology, 14, Article 1145067. https://doi.org/10.3389/fendo.2023.1145067
50. Johannes, F., Porcher, E., Teixeira, F. K., Saliba-Colombani, V., Simon, M., Agier, N., Bulski, A., Albuisson, J., Heredia, F., Audigier, P., Bouchez, D., Dillmann, C., Guerche, P., Hospital, F., & Colot, V. (2009). Assessing the impact of transgenerational epigenetic variation on complex traits. PLOS Genetics, 5(6), Article e1000530. https://doi.org/10.1371/journal.pgen.1000530
51. Klughammer, J., Romanovskaia, D., Nemc, A., Posautz, A., Seid, C. A., Schuster, L. C., Keinath, M. C., Lugo Ramos, J. S., Kosack, L., Evankow, A., Printz, D., Kirchberger, S., Ergüner, B., Datlinger, P., Fortelny, N., Schmidl, C., Farlik, M., Skjærven, K., Bergthaler, A., … Bock, C. (2023). Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species. Nature Communications, 14, Article 232. https://doi.org/10.1038/s41467-022-34828-y
52. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
53. Laine, V. N., Sepers, B., Lindner, M., Gawehns, F., Ruuskanen, S., & van Oers, K. (2023). An ecologist’s guide for studying DNA methylation variation in wild vertebrates. Molecular Ecology Resources, 23(7), 1488–1508. https://doi.org/10.1111/1755-0998.13624
54. Lamka, G. F., Harder, A. M., Sundaram, M., Schwartz, T. S., Christie, M. R., DeWoody, J. A., & Willoughby, J. R. (2022). Epigenetics in ecology, evolution, and conservation. Frontiers in Ecology and Evolution, 10, Article 871791. https://doi.org/10.3389/fevo.2022.871791
55. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
56. Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., Edsall, L., Antosiewicz-Bourget, J., Stewart, R., Ruotti, V., Millar, A. H., Thomson, J. A., Ren, B., & Ecker, J. R. (2009). Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462(7271), 315–322. https://doi.org/10.1038/nature08514
57. Liu, H., Huang, Y., Lu, S., Yuan, D., & Liu, J. (2023). Global trends of lipid metabolism research in epigenetics field: A bibliometric analysis from 2012–2021. International Journal of Environmental Research and Public Health, 20(3), Article 2382. https://doi.org/10.3390/ijerph20032382
58. Macartney, E. L., Drobniak, S. M., Nakagawa, S., & Lagisz, M. (2023). Evidence base for non-genetic inheritance of environmental exposures in non-human animals and plants: A map of evidence syntheses with bibliometric analysis. Environmental Evidence, 12, Article 1. https://doi.org/10.1186/s13750-022-00290-y
59. Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476. https://doi.org/10.1016/S0169-2070(00)00057-1
60. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014
61. Mao, Y., Zhao, K., Chen, N., Fu, Q., Zhou, Y., Kong, C., Li, P., & Yang, C. (2023). A 2-decade bibliometric analysis of epigenetics of cardiovascular disease: From past to present. Clinical Epigenetics, 15(1), Article 184. https://doi.org/10.1186/s13148-023-01603-9
62. Marzi, G., Balzano, M., Caputo, A., & Pellegrini, M. M. (2025). Guidelines for bibliometric systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. International Journal of Management Reviews, 27(1), 81–103. https://doi.org/10.1111/ijmr.12381
63. McNew, S. M., Boquete, M. T., Espinoza-Ulloa, S., Andres, J. A., Wagemaker, N. C. A. M., Knutie, S. A., Richards, C. L., & Clayton, D. H. (2021). Epigenetic effects of parasites and pesticides on captive and wild nestling birds. Ecology and Evolution, 11(12), 7713–7729. https://doi.org/10.1002/ece3.7606
64. Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
65. Mounger, J., Ainouche, M. L., Bossdorf, O., Cavé-Radet, A., Li, B., Parepa, M., Salmon, A., Yang, J., & Richards, C. L. (2021). Epigenetics and the success of invasive plants. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1826), Article 20200117. https://doi.org/10.1098/rstb.2020.0117
66. Muggeo, V. M. R. (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22(19), 3055–3071. https://doi.org/10.1002/sim.1545
67. Muggeo, V. M. R. (2016). Testing with a nuisance parameter present only under the alternative: A score-based approach with application to segmented modelling. Journal of Statistical Computation and Simulation, 86(15), 3059–3067. https://doi.org/10.1080/00949655.2016.1149855
68. Özcan, F., Hangül, C., & Özdoğan, H. (2025). Diyabet ve myricetin arasındaki ilişki: Bibliyometrik analiz. Türkiye Klinikleri Journal of Medical Sciences, 45(4), 238–248. https://doi.org/10.5336/medsci.2025-110238
69. Özdoğan, H., Sevim, G., & Üncü, Y. A. (2025). A bibliometric analysis of cross-section calculations. Journal of Nuclear Sciences, 9(2), 32–42. https://doi.org/10.59474/nuclear.2023.65
70. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
71. Pedersen, T. L. (2024). patchwork: The composer of plots (R package version 1.3.0) [Computer software]. https://CRAN.R-project.org/package=patchwork
72. R Core Team. (2025). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
73. Richards, C. L., Alonso, C., Becker, C., Bossdorf, O., Bucher, E., Colomé-Tatché, M., Durka, W., Engelhardt, J., Gaspar, B., Gogol-Döring, A., Grosse, I., van Gurp, T. P., Heer, K., Kronholm, I., Lampei, C., Latzel, V., Mirouze, M., Opgenoorth, L., Paun, O., … Verhoeven, K. J. F. (2017). Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecology Letters, 20(12), 1576–1590. https://doi.org/10.1111/ele.12858
74. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. https://doi.org/10.1093/biomet/71.3.599
75. Schauberger, P., & Walker, A. (2024). openxlsx: Read, write and edit xlsx files (R package version 4.2.7) [Computer software]. https://CRAN.R-project.org/package=openxlsx
76. Schrey, A. W., Alvarez, M., Foust, C. M., Kilvitis, H. J., Lee, J. D., Liebl, A. L., Martin, L. B., Richards, C. L., & Robertson, M. (2013). Ecological epigenetics: Beyond MS-AFLP. Integrative and Comparative Biology, 53(2), 340–350. https://doi.org/10.1093/icb/ict012
77. Sepers, B., Verhoeven, K. J. F., & van Oers, K. (2024). Early developmental carry-over effects on exploratory behaviour and DNA methylation in wild great tits (Parus major). Evolutionary Applications, 17(3), e13664. https://doi.org/10.1111/eva.13664
78. Simpson, J. T., Workman, R. E., Zuzarte, P. C., David, M., Dursi, L. J., & Timp, W. (2017). Detecting DNA cytosine methylation using nanopore sequencing. Nature Methods, 14, 407–410. https://doi.org/10.1038/nmeth.4184
79. Slowikowski, K. (2024). ggrepel: Automatically position non-overlapping text labels (R package version 0.9.6) [Computer software]. https://CRAN.R-project.org/package=ggrepel
80. Šrut, M. (2021). Ecotoxicological epigenetics in invertebrates: Emerging tool for the evaluation of present and past pollution burden. Chemosphere, 282, Article 131026. https://doi.org/10.1016/j.chemosphere.2021.131026
81. Tattershall, E., Nenadic, G., & Stevens, R. D. (2021). Modelling trend life cycles in scientific research using the Logistic and Gompertz equations. Scientometrics, 126(11), 9113–9132. https://doi.org/10.1007/s11192-021-04137-0
82. Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011). The ubiquity of small-world networks. Brain Connectivity, 1(5), 367–375. https://doi.org/10.1089/brain.2011.0038
83. Tjørve, K. M. C., & Tjørve, E. (2017). The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLOS ONE, 12(6), Article e0178691. https://doi.org/10.1371/journal.pone.0178691
84. Trapletti, A., & Hornik, K. (2024). tseries: Time series analysis and computational finance (R package version 0.10-61) [Computer software]. https://CRAN.R-project.org/package=tseries
85. Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R., & Legendre, F. (2017). Taxonomic bias in biodiversity data and societal preferences. Scientific Reports, 7, Article 9132. https://doi.org/10.1038/s41598-017-09084-6
86. Tsoularis, A., & Wallace, J. (2002). Analysis of logistic growth models. Mathematical Biosciences, 179(1), 21–55. https://doi.org/10.1016/S0025-5564(02)00096-2
87. Üncü, Y. A., Özdoğan, H., & Sevim, G. (2025). Bibliometric analysis of diffuse optical tomography studies between 1994–2024. Cumhuriyet Science Journal, 46(3), 435–446. https://doi.org/10.17776/csj.1637597
88. Verhoeven, K. J. F., Jansen, J. J., van Dijk, P. J., & Biere, A. (2010). Stress-induced DNA methylation changes and their heritability in asexual dandelions. New Phytologist, 185(4), 1108–1118. https://doi.org/10.1111/j.1469-8137.2009.03121.x
89. Verhoeven, K. J. F., vonHoldt, B. M., & Sork, V. L. (2016). Epigenetics in ecology and evolution: What we know and what we need to know. Molecular Ecology, 25(8), 1631–1638. https://doi.org/10.1111/mec.13617
90. Waddington, C. H. (1942). The epigenotype. Endeavour, 1, 18–20.
91. Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023). Forecast combinations: An over 50-year review. International Journal of Forecasting, 39(4), 1518–1547. https://doi.org/10.1016/j.ijforecast.2022.11.005
92. Weaver, I. C. G., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., Dymov, S., Szyf, M., & Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7(8), 847–854. https://doi.org/10.1038/nn1276
93. Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Springer-Verlag.
94. Wickham, H. (2023). conflicted: An alternative conflict resolution strategy (R package version 1.2.0) [Computer software]. https://CRAN.R-project.org/package=conflicted
95. Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., ... Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), Article 1686. https://doi.org/10.21105/joss.01686
96. Wickham, H., Pedersen, T. L., & Seidel, D. (2024). scales: Scale functions for visualization (R package version 1.3.0) [Computer software]. https://CRAN.R-project.org/package=scales
97. Yen, E. C., Gilbert, J. D., Balard, A., Afonso, I. O., Fairweather, K., Newlands, D., Lopes, A., Correia, S. M., Taxonera, A., Rossiter, S. J., Martín Durán, J. M., & Eizaguirre, C. (2024). DNA methylation carries signatures of sublethal effects under thermal stress in loggerhead sea turtles. Evolutionary Applications, 17(9), e70013. https://doi.org/10.1111/eva.70013
98. Zhang, X., Jiang, Y., Cai, Y., Fu, Q., & Chen, Y. (2024). Epigenetics research in eye diseases: A bibliometric analysis from 2000 to 2023. Clinical and Experimental Optometry, 107(6), 649–656. https://doi.org/10.1080/08164622.2023.2261929
99. Zhong, X., & Chen, D. (2025). Global research trends on epigenetics and cancer: A bibliometric analysis. Medicine, 104(33), Article e43197. https://doi.org/10.1097/MD.0000000000043197