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Species richness and Spatial distribution of three Pieridae subfamilies across mainland China under past and future climates

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Abstract

The family Pieridae (Lepidoptera: Pieridae) is known for its ecological and conservation significance; however, little is known about its spatial distribution pattern and climate vulnerability in mainland China, complicating the formulation of effective conservation strategies. Pierinae and Coliadinae are widely distributed across most parts of the research zone, especially in the southern regions. Conversely, Dismorphiinae is mainly distributed in the west-central and northeastern parts. Pierinae and Coliadinae flourished over a wider range of elevations in open environments with warmer and more humid habitats, whereas Dismorphiinae is restricted to a narrow elevation range in forested areas with cooler and drier habitats. Therefore, it was necessary to study their distribution patterns separately. The MaxEnt model was applied to analyze the influence of bioclimatic variables on their distribution throughout three historical eras: the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Current (1970–2000). Pierinae and Coliadinae showed a uniform increase in overall highly suitable habitats, while Dismorphiinae showed an initial increase and then a decrease. Due to global warming, all three subfamilies might experience contraction in highly suitable habitats. Most Pieridae species are projected to experience shrinkage in highly suitable habitats, leading to decreased species diversity. These findings highlight divergent historical distribution patterns and habitat preferences among Pieridae subfamilies, yet project a shared vulnerability to future habitat contraction under climate warming.

Data availability

The data presented in this study are available on request from both corresponding authors.

References

  1. Wang, W. L. et al. Butterfly Conservation in China: From Science to Action. Insects 11, 661. https://doi.org/10.3390/insects11100661 (2020).

  2. Thomas, J. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philos. Trans. R Soc. B. 360, 339–357. https://doi.org/10.1098/rstb.2004.1585 (2005).

    Google Scholar 

  3. Yu, X. T. et al. Species richness of papilionidae butterflies (Lepidoptera: Papilionoidea) in the Hengduan mountains and its future shifts under climate change. Insects 14, 259. https://doi.org/10.3390/insects14030259 (2023).

    Google Scholar 

  4. Min, W. et al. Species diversity of butterflies in Shimentai nature Reserve, Guangdong. Biodiv Sci. 11, 441–453. https://doi.org/10.17520/biods.2003052 (2003).

    Google Scholar 

  5. Pollard, E. Temperature, rainfall and butterfly numbers. J. Appl. Ecol. 25, 819–828. https://doi.org/10.2307/2403748 (1988).

    Google Scholar 

  6. Svancara, L., Abatzoglou, J. & Waterbury, B. Modeling current and future potential distributions of milkweeds and the monarch butterfly in Idaho. Front. ecol. evol. 7, 168. https://doi.org/10.3389/fevo.2019.00168 (2019).

    Google Scholar 

  7. Minter, M. et al. Past, current, and potential future distributions of unique genetic diversity in a cold-adapted mountain butterfly. Ecol. Evol. 10, 11155–11168. https://doi.org/10.1002/ece3.6755 (2020).

    Google Scholar 

  8. Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872. (2007).

    Google Scholar 

  9. Gibbs, M., Wiklund, C. & Van Dyck, H. Temperature, rainfall and butterfly morphology: does life history theory match the observed pattern? Ecography 34, 336–344. https://doi.org/10.1111/j.1600-0587.2010.06573.x (2010).

    Google Scholar 

  10. Bartonova, A. et al. Range dynamics of palaearctic steppe species under glacial cycles: the phylogeography of Proterebia Afra (Lepidoptera: nymphalidae: Satyrinae). Biol. J. Linn. Soc. 125, 867–884. https://doi.org/10.1093/biolinnean/bly136 (2018).

    Google Scholar 

  11. Taberlet, P., Fumagalli, L., Wust-Saucy, A. G. & Cosson, J. F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464. https://doi.org/10.1046/j.1365-294x.1998.00289.x (2002).

    Google Scholar 

  12. Schoville, S., Stuckey, M. & Roderick, G. Pleistocene origin and population history of a neoendemic alpine butterfly. Mol. Ecol. 20, 1233–1247. https://doi.org/10.1111/j.1365-294X.2011.05003.x (2011).

    Google Scholar 

  13. Sherpa, S. et al. Population decline at distribution margins: assessing extinction risk in the last glacial relictual but still functional metapopulation of a European butterfly. Divers. Distrib. 28, 271–290. https://doi.org/10.1111/ddi.13460 (2022).

    Google Scholar 

  14. Kebaili, C., Sherpa, S., Rioux, D. & Després, L. Demographic inferences and Climatic niche modelling shed light on the evolutionary history of the emblematic cold-adapted Apollo butterfly at regional scale. Mol. Ecol. 31, 448–466. https://doi.org/10.1111/mec.16244 (2021).

    Google Scholar 

  15. Kumar, P. Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using maxent modelling: limitations and challenges. Biodivers. Conserv. 21, 1251–1266. https://doi.org/10.1007/s10531-012-0279-1 (2012).

    Google Scholar 

  16. Wu, C. S. & Hsu, Y. F. Butterflies of ChinaVol. 41442–2036 (The Straits Publishing & Distributing Group, 2017).

  17. Simon, L. Encyclopedia of Biodiversity. Vol. 1, 943Princeton university, Academic Press, (2013).

  18. Powell, J. A., Resh, V. H. & Cardé, R. T. Lepidoptera. In Encyclopedia of Insects 2nd edn (Academic, 2009).

  19. Vane-Wright, D. & de Jong, R. The butterflies of sulawesi: annotated checklist for a critical Island fauna. Zool. Verh Leiden. 343, 3–267 (2003).

    Google Scholar 

  20. Ding, C. & Zhang, Y. L. Phylogenetic relationships of Pieridae (Lepidoptera: Papilionoidea) in China based on seven gene fragments: phylogenetic relationships of Pieridae. Entomol. Sci. 20, 15–23. https://doi.org/10.1111/ens.12214 (2016).

    Google Scholar 

  21. Opler, P. A. A Field Guide To Western Butterflies 2nd edn (Houghton Mifflin Company, 1998).

  22. Brock, J. P. & Kaufman, K. Kaufman Field Guide To Butterflies of North America (Houghton Mifflin Company, 2003).

  23. Shapiro, A. M. The biology and zoogeography of the legume-feeding Patagonian-Fuegian white butterfly Tatochila Theodice (Lepidoptera: Pieridae). J. N. Y. Entomol. Soc. 251–260 (1991).

  24. Shapiro, A. M., Forister, M. L. & Fordyce, J. A. Extreme high-altitude Asian and Andean Pierid butterflies are not each others’ closest relatives. Arct. Antarct. Alp. Res. 39, 137–142 (2007).

    Google Scholar 

  25. Shapiro, A. M. Behavioral and ecological observations of Peruvian High-Andean Pierid butterflies (Lepidoptera). Stud. Neotrop. Fauna Environ. 20, 1–13. https://doi.org/10.1080/01650528509360 (1985).

    Google Scholar 

  26. Shapiro, A. M. Developmental and phenotypic responses to photoperiod and temperature in an Equatorial montane butterfly, Tatochila xanthodice (Lepidoptera: Pieridae). Biotropica 297–301. https://doi.org/10.2307/2387682 (1978).

  27. Tshikolovets, V. V., Yakovlev, R. V. & Bálint, Z. The Butterflies of Mongolia (Tshikolovets, 2009).

  28. Tshikolovets, V. V., Yakovlev, R. V. & Kosterin, O. E. The Butterflies of Altai, Sayans and Tuva (Tshikolovets, 2009). (South Siberia.

  29. Laiho, J. & Ståhls, G. DNA barcodes identify central Asian Colias butterflies (Lepidoptera, Pieridae). Zookeys 30, 175–196. https://doi.org/10.3897/zookeys.365.5879 (2013).

    Google Scholar 

  30. Yata, O. Butterflies of the South East Asian IslandsVol. 3 (Plapac Co. Ltd., 1985).

  31. Leong, J. V. et al. Around the world in 26 million years: diversification and biogeography of Pantropical Grass-Yellow Eurema butterflies (Pieridae: Coliadinae). J. Biogeogr. 52, e15107. https://doi.org/10.1111/jbi.15107 (2025).

    Google Scholar 

  32. Lukhtanov, V. A. Karyotype evolution and systematics of higher taxa of Pieridae (Lepidoptera) of the world. Ent Obozr. 70, 619–636 (1991).

    Google Scholar 

  33. Lamas, G. Twenty-five new Neotropical dismorphiinae (Lepidoptera-Pieridae). Rev. Peru Entomol. 44, 17–36 (2004).

    Google Scholar 

  34. Soberón, J. M., Llorente, J. B. & Oñate, L. The use of specimen-label databases for conservation purposes: an example using Mexican papilionid and Pierid butterflies. Biodivers. Conserv. 9, 1441–1466. https://doi.org/10.1023/A:1008987010383 (2000).

    Google Scholar 

  35. Zheng, P. China’s Geography142 (China Intercontinental, 2010).

  36. Machine, W. Regions of Chinese food-styles/flavors of cooking. (2024). https://web.archive.org/web/20090416203009/http://www.kas.ku.edu:80/service-learning/projects/chinese_food/regional_cuisine. html.

  37. Zhu, H. The tropical forests of Southern China and conservation of biodiversity. Bot. Rev. 83, 87–105. https://doi.org/10.1007/s12229-017-9177-2 (2017).

    Google Scholar 

  38. Wang, Y. et al. Macro-evolutionary dynamics dominated by dispersal promote the formation of regional biodiversity hotspot‐insights from hawkmoths (Lepidoptera: Sphingidae) in South China. Divers. Distrib. 31, e13916. https://doi.org/10.1111/ddi.13916 (2025).

    Google Scholar 

  39. Forti, L. R. & Szabo, J. K. The iNaturalist platform as a source of data to study amphibians in Brazil. Acad. Bras. Ciênc. 95, e20220828. https://doi.org/10.1590/0001-3765202320220828 (2023).

    Google Scholar 

  40. Su, J. et al. The distribution pattern and species richness of scorpionflies (Mecoptera: Panorpidae). Insects 14, 332. https://doi.org/10.3390/insects14040332 (2023).

    Google Scholar 

  41. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km Spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).

    Google Scholar 

  42. Chen, Y., Guo, D., Cao, W. & Li, Y. Changes in net primary productivity and factor detection in china’s yellow river basin from 2000 to 2019. Remote Sens. 15, 2798. https://doi.org/10.3390/rs15112798 (2023).

    Google Scholar 

  43. Bai, W. et al. Analysis of Spatial and Temporal variation of vegetation NPP in Daning river basin and its driving forces. Int. J. Remote Sens. 44, 6194–6218. (2023).

    Google Scholar 

  44. Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).

    Google Scholar 

  45. Moss, R. et al. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies: IPCC Expert Meeting Report (Noordwijkerhout). http://www.ipcc.ch/pdf/supporting-material/expert-meeting-ts-scenarios.pdf.IP (2008) .

  46. John, W. et al. Report of 2.6 Versus 2.9 Watts/m2 RCPP Evaluation Panel (University of Maryland, Joint Global Change Research Institute, 2009).

  47. Vilela, B., Villalobos, F. & LetsR A new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234. https://doi.org/10.1111/2041-210X.12401 (2015).

    Google Scholar 

  48. Santana, P. A. Jr, Kumar, L., Da Silva, R. S., Pereira, J. L. & Picanço, M. C. Assessing the impact of climate change on the worldwide distribution of Dalbulus Maidis (DeLong) using maxent. Pest Manag. Sci. 75, 2706–2715. https://doi.org/10.1002/ps.5379 (2019).

    Google Scholar 

  49. Wei, J., Peng, L., He, Z., Lu, Y. & Wang, F. Potential distribution of two invasive pineapple pests under climate change. Pest Manag. Sci. 76, 1652–1663. https://doi.org/10.1002/ps.5684 (2020).

    Google Scholar 

  50. Warren, D. L., Wright, A. N., Seifert, S. N. & Shaffer, H. B. Incorporating model complexity and Spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Divers. Distrib. 20, 334–343. https://doi.org/10.1111/ddi.12160 (2014).

    Google Scholar 

  51. Zhou, B. J., Ma, C. L., Gao, C., Han, D. N. & Chai, Y. Impacts of climate change on species distribution patterns of Polyspora sweet in China. Ecol. Evol. 12, e9516. https://doi.org/10.1002/ece3.9516 (2022).

    Google Scholar 

  52. Yang, X., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia Adhatoda L. in lesser Himalayan foothills. Ecol. Eng. 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 (2013).

    Google Scholar 

  53. Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).

    Google Scholar 

  54. Hijmans, R. J. & Elith, J. Spatial distribution models. Vol. 10, (2019).

  55. Zhang, X. F., Nizamani, M. M., Jiang, C., Fang, F. Z. & Zhao, K. K. Potential planting regions of Pterocarpus Santalinus (Fabaceae) under current and future climate in China based on maxent modeling. Ecol. Evol. 14, e11409. https://doi.org/10.1002/ece3.11409 (2024).

    Google Scholar 

  56. Lu, S. et al. Patterns of tree species richness in Southwest China. Environ. Monit. Assess. 193, 97. https://doi.org/10.1007/s10661-021-08872-y (2021).

    Google Scholar 

  57. Xie, D. et al. Diversity of higher plants in China. J. Syst. Evol. 59, 1111–1123. https://doi.org/10.1111/jse.12758 (2021).

    Google Scholar 

  58. Ômura, H. Plant secondary metabolites in host selection of butterfly. In Chemical Ecology of Insects. 3–27 (CRC, Boca Raton, Florida, (2018).

    Google Scholar 

  59. Castro-Gerardino, D. J. & Llorente-Bousquets, J. Comparative exploration of antennae in Pseudopontia, and antennal clubs of the tribes leptideini and dismorphiini (Lepidoptera: Pieridae). Zootaxa 4347, 401–445 (2017).

    Google Scholar 

  60. Scott, J. A. The Butterflies of North America: A Natural History and Field Guide584 (Stanford University Press, 1992).

  61. Shanks, K., Senthilarasu, S., ffrench-Constant, R. H. & Mallick, T. K. White butterflies as solar photovoltaic concentrators. Sci. Rep. 5, 12267. https://doi.org/10.1038/srep12267 (2015).

    Google Scholar 

  62. Bladon, A. J. et al. How butterflies keep their cool: physical and ecological traits influence thermoregulatory ability and population trends. J. Anim. Ecol. 89, 2440–2450. https://doi.org/10.1111/1365-2656.13319 (2020).

    Google Scholar 

  63. Lamas, G. The dismorphiinae (Pieridae) of Mexico, central America and the Antilles. Mexi Soc. Lepi. 5, 3–37 (1979).

    Google Scholar 

  64. Hill, G. M., Kawahara, A. Y., Daniels, J. C., Bateman, C. C. & Scheffers, B. R. Climate change effects on animal ecology: butterflies and moths as a case study. Biol. Rev. 96, 2113–2126. https://doi.org/10.1111/brv.12746 (2021).

    Google Scholar 

  65. Oberhauser, K. & Peterson, A. Modelling current and future potential wintering distributions of Eastern North American monarch butterflies. Proc. Natl. Acad. Sci. U S A. 100, 14063–14068. https://doi.org/10.1073/pnas.2331584100 (2003).

    Google Scholar 

  66. Zavaleta, E. S., Shaw, M. R., Chiariello, N. R., Mooney, H. A. & Field, C. B. Additive effects of simulated climate changes, elevated CO2, and nitrogen deposition on grassland diversity. Proc. Natl. Acad. Sci. U S A. 100, 7650–7654. https://doi.org/10.1073/pnas.0932734100 (2003).

    Google Scholar 

  67. Trotter, R. T., Cobb, N. S. & Whitham, T. G. Arthropod community diversity and trophic structure: a comparison between extremes of plant stress. Ecol. Entomol. 33, 1–11. https://doi.org/10.1111/j.1365-2311.2007.00941.x (2008).

    Google Scholar 

  68. Kishimoto-Yamada, K. & Itioka, T. Consequences of a severe drought associated with an El Niño-Southern Oscillation on a light-attracted leaf-beetle (Coleoptera, Chrysomelidae) assemblage in Borneo. J. Trop. Ecol. 24, 229–233. https://doi.org/10.1017/S0266467408004811 (2008).

    Google Scholar 

  69. Nielsen, E. T. & Nielsen, H. T. Temperatures preferred by the Pierid Ascia Monuste L. Ecology 40, 181–185. https://doi.org/10.2307/1930027 (1959).

    Google Scholar 

  70. Advani, N. K., Parmesan, C. & Singer, M. C. Takeoff temperatures in Melitaea Cinxia butterflies from latitudinal and elevational range limits: A potential adaptation to solar irradiance. Ecol. Entomol. 44, 389–396. https://doi.org/10.1111/een.12714 (2019).

    Google Scholar 

  71. Hayes, M. P., Hitchcock, G. E., Knock, R. I., Lucas, C. B. H. & Turner, E. C. Temperature and territoriality in the Duke of Burgundy butterfly, Hamearis Lucina. J. Insect Conserv. 23, 739–750. https://doi.org/10.1007/s10841-019-00166-6 (2019).

    Google Scholar 

  72. Wang, X. et al. Greater impacts from an extreme cold spell on tropical than temperate butterflies in Southern China. Ecosphere 7, e01315. https://doi.org/10.1002/ecs2.1315 (2016).

    Google Scholar 

  73. Ehrlich, P. R. et al. Extinction, reduction, stability and increase: the responses of checkerspot butterfly (Euphydryas) populations to the California drought. Oecologia 46, 101–105. https://doi.org/10.1007/BF00346973 (1980).

    Google Scholar 

  74. Huntley, B. et al. Explaining patterns of avian diversity and endemicity: climate and biomes of Southern Africa over the last 140,000 years. J. Biogeogr. 43, 874–886. https://doi.org/10.1111/jbi.12714 (2016).

    Google Scholar 

  75. Alves, W. F. et al. Connectivity and climate influence diversity–stability relationships across Spatial scales in European butterfly metacommunities. Glob Ecol. Biogeogr. 33, 1–16. https://doi.org/10.1111/geb.13896 (2024).

    Google Scholar 

  76. Chowdhury, S., Fuller, R. A., Dingle, H., Chapman, J. W. & Zalucki, M. P. Migration in butterflies: a global overview. Biol. Rev. 96, 1462–1483. https://doi.org/10.1111/brv.12714 (2021).

    Google Scholar 

  77. Kühne, G., Kosuch, J., Hochkirch, A. & Schmitt, T. Extra-Mediterranean glacial refugia in a mediterranean faunal element: the phylogeography of the chalk-hill blue Polyommatus Coridon (Lepidoptera, Lycaenidae). Sci. Rep. 7, 43533. https://doi.org/10.1038/srep43533 (2017).

    Google Scholar 

  78. Habel, J. C., Lens, L., Rödder, D. & Schmitt, T. From Africa to Europe and back: refugia and range shifts cause high genetic differentiation in the marbled white butterfly Melanargia Galathea. BMC Evol. Biol. 11, 1–15. https://doi.org/10.1186/1471-2148-11-215 (2011).

    Google Scholar 

  79. Foster, P. The potential negative impacts of global climate change on tropical montane cloud forests. Earth Sci. Rev. 55, 73–106. https://doi.org/10.1016/S0012-8252(01)00056-3 (2001).

    Google Scholar 

  80. Cannone, N., Sgorbati, S. & Guglielmin, M. Unexpected impacts of climate change on alpine vegetation. Front. Ecol. Environ. 5, 360–364. https://doi.org/10.1890/1540-9295 (2007).

    Google Scholar 

  81. Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change. Ecol. lett. 8, 1138–1146. https://doi.org/10.1111/j.1461-0248.2005.00824.x (2005).

    Google Scholar 

  82. Liang, Q. et al. Shifts in plant distributions in response to climate warming in a biodiversity hotspot, the Hengduan mountains. J. Biogeogr. 45, 1334–1344. https://doi.org/10.1111/jbi.13229 (2018).

    Google Scholar 

  83. Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110. https://doi.org/10.1126/sciadv.abe1110 (2021).

    Google Scholar 

  84. Hu, R. et al. Shifts in bird ranges and conservation priorities in China under climate change. PLoS One. 15, e0240225. https://doi.org/10.1371/journal.pone.0240225 (2020).

    Google Scholar 

  85. Lamsal, P., Kumar, L., Aryal, A. & Atreya, K. Future climate and habitat distribution of Himalayan musk deer (Moschus chrysogaster). Ecol. Inf. 44, 101–108. https://doi.org/10.1016/j.ecoinf.2018.02.004 (2018).

    Google Scholar 

  86. Yang, F., Hu, J. & Wu, R. Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China. Sci. Rep. 6, 30753. https://doi.org/10.1038/srep30753 (2016).

    Google Scholar 

  87. Ma, F. Z. et al. Progress in construction of China butterfly diversity observation network (China BON-Butterflies). J. Ecol. Rural Environ. 34, 27–36. https://doi.org/10.11934/j.issn.1673-4831.2018.01.004 (2018).

    Google Scholar 

  88. Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., & Ram, K. rgbif: Interface to the global biodiversity informationfacility API. R package version 3.7.2. https://CRAN.R-project.org/package=rgbif, (2022).

  89. Barve, V. & Hart, E. rinat: Access ‘iNaturalist’ Data Through APIs. R package version 0.1.9. https://docs.ropensci.org/rinat/ (2025)

  90. Maclean, I. M. D. & Wilson, R. J. Recent ecological responses to climate change support predictions of high extinction risk. Proc. Natl. Acad. Sci. 108,12337–12342. https://doi.org/10.1073/pnas.1017352108 (2011).

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Acknowledgements

We would like to express our sincere gratitude to Dr. Rehan Zafar for providing invaluable guidance in the analysis of this study. We also thank Dr. Hashmat Khan and Dr. Haroon Khan for their thoughtful review and constructive feedback on the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (grant number 32471563).

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Authors and Affiliations

  1. College of Life Sciences, Northwest University, Xi’an, 710069, People’s Republic of China

    Riaz Hussain, Panpan Miao, Lianxi Xing & Yuan Hua

  2. College of Geographical Sciences, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, 450046, People’s Republic of China

    Adnanul Rehman

  3. Xi’an Botanical Garden of Shaanxi Province (Institute of Botany of Shaanxi Province), Xi’an, 710061, People’s Republic of China

    Lijun Fang

  4. Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, 710069, People’s Republic of China

    Lianxi Xing

  5. Key Laboratory of Resource Biology and Biotechnology in Western China, Northwest University, Ministry of Education, Xi’an, 710069, People’s Republic of China

    Lianxi Xing

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  1. Riaz Hussain
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Contributions

R.H. and Y.H: Conceptualization; R.H, Y.H, L.F, A.R, and P.M: methodology and experiments; R.H. and Y.H: data analysis; R.H: writing—original draft preparation; Y.H, L.F, and A.R: writing—review and editing; R.H: visualization; L.X. and Y.H: supervision; L.X: project administration; L.X: funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Hussain, R., Miao, P., Rehman, A. et al. Species richness and Spatial distribution of three Pieridae subfamilies across mainland China under past and future climates.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-21555-9

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Keywords

  • Pierinae
  • Coliadinae
  • Dismorphiinae
  • MaxEnt model
  • Species richness
  • Distribution pattern
  • Climate change

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