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Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations

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Abstract

Pteridium aquilinum is a medicinally important fern with a limited range in northern Iran, increasingly threatened by climate change. Using morphological, genetic, and environmental data, we assessed differentiation, adaptive capacity, and vulnerability across 11 populations. Factor analysis of mixed data (FAMD) identified stipe indument, pinnule shape, and pinnae number as key traits distinguishing populations. Redundancy and association analyses (RDA/CCA) revealed strong links between both morphological and genetic variation and climatic gradients, particularly temperature and humidity, indicating local adaptation. Several SCoT loci were detected as adaptive outliers. Spatial PCA showed that variation is shaped by both global and local spatial factors, forming clines and local variants. Populations varied in sensitivity and adaptive capacity; populations 2, 3, 7, and 8 exhibited the lowest adaptive indices and highest vulnerability. Connectivity modeling suggested that while some populations (e.g., 2, 4, and 6) may maintain or slightly improve connectivity, others risk isolation under future climates. Structural equation modeling (SEM) indicated a positive genetic contribution to adaptation, while differential equation modeling (DEM) predicted logistic growth with temporary instability and genetic decline in vulnerable groups. Overall, findings highlight spatially uneven adaptive responses and recommend targeted conservation through connectivity enhancement, assisted gene flow, and ex-situ preservation of adaptive genotypes.

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Data availability

The datasets used and/ or analyzed during the current study available from the corresponding author on reasonable request.

References

  1. IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability (Cambridge University Press, 2022).

  2. Kelly, S. A., Panhuis, T. M. & Stoehr, A. M. Phenotypic plasticity: molecular mechanisms and adaptive significance. Compreh Physiol. 9 (2), 259–303 (2019).

    Google Scholar 

  3. Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Google Scholar 

  4. Scheiner, S. M., Barfield, M. & Holt, R. D. The genetics of phenotypic plasticity. XVII. Response to climate change. Ecol. Evol. 9 (22), 12375–12389 (2019).

    Google Scholar 

  5. Christmas, M. J., Breed, M. F. & Lowe, A. J. Constraints to and conservation implications for climate change adaptation in plants. Conserv. Genet. 17 (2), 305–320 (2016).

    Google Scholar 

  6. Corlett, R. T. Climate change and the evolution of the next generation of tropical forest trees. Perspect. Plant. Ecol. Evol. Syst. 13 (1), 163–172 (2011).

    Google Scholar 

  7. Guan, B., Gao, J., Chen, W., Gong, X. & Ge, G. The effects of climate change on landscape connectivity and genetic clusters in a small subtropical and warm-temperate tree. Front. Plant. Sci. 12, 671336 (2021).

    Google Scholar 

  8. Foden, W. B. et al. Climate change vulnerability assessment of species. Wiley Interdiscip Rev. Clim. Change. 4 (3), 159–181 (2013).

    Google Scholar 

  9. Mair, L., O’Brien, G. S. D. W. & Purvis, A. The forgotten half of the species-area relationship. Ecol. Appl. 31 (8), e02447 (2021).

  10. Guisan, A., Edwards, T. C. & Hastie, T. Generalized linear and non-linear models for predicting species distributions. Ecol. Modell. 257, 1–17 (2013).

    Google Scholar 

  11. Moran, R. C. Diversity, biogeography, and floristics. In Biology and Evolution of Ferns and Lycophytes, 367–395 (Cambridge University Press, Cambridge, (2008).

    Google Scholar 

  12. Higgins, M. A. et al. Geological control of floristic composition in Amazonian forests. J. Biogeogr. 38 (11), 2136–2149 (2011).

    Google Scholar 

  13. Karst, J., Gilbert, B. & Lechowicz, M. J. Fern community assembly. Ecology 86 (9), 2473–2486 (2005).

    Google Scholar 

  14. Della, A. P. & Falkenberg, D. D. B. Pteridophytes as ecological indicators: an overview. Hoehnea 46 (1), e522018 (2019).

    Google Scholar 

  15. Sheidai, M., Alaeifar, M. & Koohdar, F. Integrating Geostatistical approaches into landscape genetics. Plant Mol. Biol. Rep. 1–12 (2025).

  16. Christenhusz, M. et al. Pteridium Pinetorum (The IUCN Red List of Threatened Species, 2017).

  17. CABI. Pteridium aquilinum (bracken). CABI Compendium. (2020).

  18. Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292 (5517), 673–679 (2001).

    Google Scholar 

  19. Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant. Sci. 15 (12), 684–692 (2010).

    Google Scholar 

  20. Bradshaw, A. D. Unraveling phenotypic plasticity. New. Phytol. 170 (4), 644–648 (2006).

    Google Scholar 

  21. Reed, T. E., Schindler, D. E. & Waples, R. S. Phenotypic plasticity and evolution in population persistence. Conserv. Biol. 25 (1), 56–63 (2011).

    Google Scholar 

  22. Sheidai, M., Alaeifar, M. & Koohdar, F. PLS-SEM in plant ecological studies. Ecol. Modell. 500, 110–125 (2024).

    Google Scholar 

  23. Cruzan, M. B. & Hendrickson, E. C. Landscape genetics of plants. Plant. Commun. 1 (6), 100100 (2020).

    Google Scholar 

  24. Thurman, L. L., Stein, B. & Beever, E. A. Adaptive capacity of species to climate change. Front. Ecol. Environ. 18 (9), 499–507 (2020).

    Google Scholar 

  25. Fortini, L., Loehman, R. A. & Holsinger, L. M. Adaptive capacity of Pinus radiata. Glob Change Biol. 23 (1), 160–170 (2017).

    Google Scholar 

  26. Arnold, P. A., Kruuk, L. E. B. & Nicotra, A. B. Analyzing plant phenotypic plasticity. New. Phytol. 22 (3), 1235–1241 (2019).

    Google Scholar 

  27. Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).

    Google Scholar 

  28. Thuiller, W. et al. Predicting global change impacts on plants. Perspect. Plant. Ecol. Evol. Syst. 9 (3–4), 137–152 (2008).

    Google Scholar 

  29. Jones, M. M. et al. Environmental heterogeneity and ferns. J. Ecol. 94 (1), 181–195 (2006).

    Google Scholar 

  30. FAO. GIEWS Country Brief: Iran (FAO, 2020).

  31. Zohary, M. Geobotanical Foundations of the Middle East (Gustav Fischer, 1973).

  32. Alaeifar, M., Sheidai, M. & Koohdar, F. Genetic diversity of Pteridium aquilinum. Plant Genet. Resour. 1–8 (2025).

  33. Ahmed, N. et al. Purchase intention toward organic food. J. Environ. Plan. Manag. 64 (5), 796–822 (2021).

    Google Scholar 

  34. Pagès, J. Multiple Factor Analysis by Example Using R (CRC, 2014).

  35. Lê, S., Josse, J. & Husson, F. FactoMineR: an R package. J. Stat. Softw. 25, 1–18 (2008).

    Google Scholar 

  36. Husson, F., Lê, S. & Pagès, J. Exploratory Multivariate Analysis Using R (CRC, 2011).

  37. Jolliffe, I. Principal component analysis. In International Encyclopedia of Statistical Science, 1094–1096 (Springer, (2011).

    Google Scholar 

  38. Legendre, P. & Legendre, L. F. L. Numerical Ecology (Elsevier, 2012).

  39. Forester, B. R. et al. Detecting multilocus adaptation. Mol. Ecol. 27 (9), 2215–2233 (2018).

    Google Scholar 

  40. Zuur, A. F. et al. Data exploration protocol. Methods Ecol. Evol. 1 (1), 3–14 (2010).

    Google Scholar 

  41. Jombart, T., Devillard, S. & Balloux, F. Spatial analysis of genetic variation. Genetics 178 (3), 1679–1691 (2008).

    Google Scholar 

  42. Hoban, S. et al. Genomic basis of local adaptation. Am. Nat. 188 (4), 379–397 (2016).

    Google Scholar 

  43. François, O. et al. Controlling false discoveries. Mol. Ecol. 25 (2), 454–469 (2016).

    Google Scholar 

  44. Andrews, K. R. et al. RADseq in genomics. Nat. Rev. Genet. 17 (2), 81–92 (2016).

    Google Scholar 

  45. Nussey, D. H. et al. Phenotypic plasticity in natural populations. J. Evol. Biol. 20 (2), 891–903 (2007).

    Google Scholar 

  46. Hadfield, J. D. MCMC methods for GLMM. (2009).

  47. Sexton, J. P. et al. Isolation by environment or distance. Evolution 68 (1), 1–15 (2014).

    Google Scholar 

  48. Sunday, J. M. et al. Thermal tolerance in ectotherms. Proc. R Soc. B. 278 (1713), 1823–1830 (2011).

    Google Scholar 

  49. Valladares, F., Matesanz, S. & Niinemets, Ü. Environmental stress and evolution. Biol. Rev. 89 (3), 564–582 (2014).

    Google Scholar 

  50. Dawson, T. P. et al. Biodiversity conservation in changing climate. Science 332 (6025), 53–58 (2011).

    Google Scholar 

  51. Young, B. E. et al. Climate change vulnerability index. Wildl. Soc. Bull. 39 (1), 174–181 (2015).

    Google Scholar 

  52. Wessels, C., Merow, C. & Trisos, C. H. Climate change risk to wild food plants. Reg. Environ. Change. 21 (2), 29 (2021).

    Google Scholar 

  53. Rinnan, D. S. & Lawler, J. Climate-niche factor analysis. Ecography 42 (9), 1494–1503 (2019).

    Google Scholar 

  54. Gienapp, P. et al. Environmental vs. genetic effects. Ecol. Lett. 11 (7), 633–643 (2008).

    Google Scholar 

  55. Wasserman, D. et al. EPA guidance on suicide treatment. Eur. Psychiatry. 27 (2), 129–141 (2012).

    Google Scholar 

  56. Krosby, M. et al. Ecological connectivity. Conserv. Biol. 24 (6), 1686–1689 (2010).

    Google Scholar 

  57. Fick, S. E. & Hijmans, R. J. WorldClim 2. Int. J. Climatol. 37 (12), 4302–4315 (2017).

    Google Scholar 

  58. Dormann, C. F. et al. Collinearity Rev. Ecography 36 (1), 27–46 (2013).

    Google Scholar 

  59. Dijkstra, E. W. A note on two problems in connection with graphs. Numer. Math. 1 (1), 269–271 (1959).

    Google Scholar 

  60. Inoue, K. & Berg, D. J. Climate change and Cumberlandia monodonta. Glob Change Biol. 23 (1), 94–107 (2017).

    Google Scholar 

  61. McRae, B. H. Isolation by resistance. Evolution 60 (8), 1551–1561 (2006).

    Google Scholar 

  62. Manel, S. et al. Landscape genetics. Trends Ecol. Evol. 18 (4), 189–197 (2003).

    Google Scholar 

  63. Balkenhol, N. et al. Landscape Genetics: Concepts, Methods, Applications (Wiley-Blackwell, 2015).

  64. Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470 (7335), 479–485 (2011).

    Google Scholar 

  65. Spear, S. F. et al. Resistance surfaces in landscape genetics. Mol. Ecol. 19 (17), 3576–3591 (2010).

    Google Scholar 

  66. Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2010).

  67. Soetaert, K. et al. DeSolve package. J. Stat. Softw. 33 (9), 1–25 (2010).

    Google Scholar 

  68. Ixaru, L. G. & Vanden Berghe, G. Runge–Kutta solvers. In Exponential Fitting, 165–186 (Springer, 2004).

    Google Scholar 

  69. Transtrum, M. K. & Sethna, J. P. Improvements to Levenberg–Marquardt. https://arxiv.org/abs/1201.5885 (2012).

  70. Moré, J. J. The Levenberg–Marquardt algorithm. In Numerical Analysis, 105–116 (Springer, 1978).

    Google Scholar 

  71. Gao, X. et al. Climate change and firmiana Kwangsiensis. Ecol. Evol. 12 (8), e9165 (2022).

    Google Scholar 

  72. Mittal, S. Threats to biodiversity. Global Biodiversity Outlook 2 (United Nations Environment Programme, Nairobi, (2019).

    Google Scholar 

  73. Spathelf, P. et al. Adaptive forest management. Ann. Sci. 75, 55 (2018).

    Google Scholar 

  74. Li, Y. et al. Landscape genomics. Front. Plant. Sci. 8, 2136 (2017).

    Google Scholar 

  75. Buzatti, R. S. O. et al. Leaf trait variation. Front. Plant. Sci. 10, 1580 (2019).

    Google Scholar 

  76. Sexton, J. P. et al. Adaptive responses to climate change. Evol. Appl. 2 (2), 185–197 (2009).

    Google Scholar 

  77. Hampe, A. & Petit, R. J. Conserving biodiversity. Front. Ecol. Environ. 3 (10), 542–550 (2005).

    Google Scholar 

  78. Ghasemian, S. et al. Persian squirrel genetics. Ecol. Evol. 13 (7), e10318582 (2023).

    Google Scholar 

  79. Gholamali-Fard, N. et al. Gene flow barriers in lizards. Zool. Scr. 49 (6), 738–751 (2020).

    Google Scholar 

  80. Dolatkhahi, F. et al. Genetic diversity of Dracocephalum kotschyi. Hort Environ. Biotechnol. 60 (5), 767–777 (2019).

    Google Scholar 

  81. Matesanz, S., Gianoli, E. & Valladares, F. Global change and plant plasticity. Ann. N Y Acad. Sci. 1206 (1), 35–55 (2010).

    Google Scholar 

  82. Sultan, S. E. Phenotypic plasticity. Trends Plant. Sci. 5 (12), 537–542 (2000).

    Google Scholar 

  83. Agustí, J. & Blázquez, M. A. Plant vascular development. Cell. Mol. Life Sci. 77 (19), 3711–3728 (2020).

    Google Scholar 

  84. Ehleringer, J. R. Leaf morphology and stress. Oecologia 47 (3), 307–310 (1980).

    Google Scholar 

  85. Johnson, H. B. Plant pubescence. Bot. Rev. 41 (3), 233–258 (1975).

    Google Scholar 

  86. Levin, D. A. Role of trichomes. Q. Rev. Biol. 48 (1), 3–15 (1973).

    Google Scholar 

  87. Read, J., Sanson, G. D. & Watt, A. D. Leaf shape and function. New. Phytol. 204 (2), 263–278 (2014).

    Google Scholar 

  88. Nicotra, A. B. et al. Plasticity in changing climate. Funct. Ecol. 25 (1), 237–251 (2011).

    Google Scholar 

  89. Givnish, T. J. Leaf form significance. In Topics in Plant Population Biology. 375–404 (Cambridge University Press, 1979).

    Google Scholar 

  90. Vogel, S. Convective cooling and leaf shape. J. Exp. Bot. 21 (4), 91–101 (1970).

    Google Scholar 

  91. Nobel, P. S. Physicochemical and Environmental Plant Physiology (Academic, 2009).

  92. Parkhurst, D. F. & Mott, K. A. Gas exchange within leaves. Plant. Cell. Environ. 13 (7), 697–707 (1990).

    Google Scholar 

  93. Whitmore, T. C. Tropical Rain Forests of the Far East (Oxford University Press, 1984).

  94. Givnish, T. J. Leaf form. In Topics in Plant Population Biology. 375–407 (Cambridge University Press, 1978).

    Google Scholar 

  95. Habel, J. C. et al. Genetic drift in orchids. Biol. Conserv. 144 (12), 3020–3027 (2011).

    Google Scholar 

  96. Row, J. R. et al. Landscape features and salamander genetics. Conserv. Genet. 15 (3), 667–680 (2014).

    Google Scholar 

  97. Geffen, E., Anderson, M. J. & Wayne, R. K. Dispersal barriers in wolves. Mol. Ecol. 13 (10), 2481–2490 (2004).

    Google Scholar 

  98. Gosper, C. R. et al. Flora conservation and OCBIL theory. Biol. J. Linn. Soc. 133 (2), 373–393 (2021).

    Google Scholar 

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Authors and Affiliations

  1. Department of Plant Sciences and Biotechnology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran

    Masoud Sheidai, Maedeh Alaeifar & Fahimeh Koohdar

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  1. Masoud Sheidai
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  2. Maedeh Alaeifar
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  3. Fahimeh Koohdar
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Contributions

M. Sh. and F. K. Conceptualization of the project, designed the research, analysis and wrote the manuscript and M. A. collected the samples and lab work. All authors reviewed the manuscript.

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Masoud Sheidai.

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Sheidai, M., Alaeifar, M. & Koohdar, F. Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33035-1

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  • DOI: https://doi.org/10.1038/s41598-025-33035-1

Keywords

  • Multivariate statistics
  • Statistical modelling
  • SCoT markers
  • Morphometric
  • Genetic markers

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