Abstract
Cephonodes hylas, or the Coffee Bee Hawk Moth, is a significant agricultural pest that threatens crops like coffee and garden plants in Asia, Oceania, and parts of Africa. Its larvae feed on Coffea species and Gardenia, making its distribution assessment crucial for future agricultural impact and management. This study employed MaxEnt to evaluate the potential distribution of C. hylas under three socioeconomic scenarios between 2041 and 2080. The model demonstrated high accuracy, with AUC values of 0.925 and TSS values of around 0.815. Key environmental factors affecting its distribution include precipitation, isothermality, temperature, and diurnal range. Currently, C. hylas is widespread across continents except Antarctica, with notable populations in Africa and Asia. Under a low-emission scenario, highly suitable habitats are projected to increase by 6.51% by 2080, while a high-emission scenario predicts a 55.46% reduction in suitable areas. This study underscores the need for monitoring and management to address the pest’s impact amid climate change.
Similar content being viewed by others
Arabica-like flavour in a heat-tolerant wild coffee species
Heterotic potential and combining ability of Coffea arabica L
Is coffee transition to cocoa a doubtful adaptation strategy for smallholders in Mesoamerica?
Data availability
Supplementary material accompanies this paper. Any other data can be made available upon request to the corresponding author.
References
Paini, D. R., Mwebaze, P., Kuhnert, P. M. & Kriticos, D. J. Global establishment threat from a major forest pest via international shipping: Lymantria dispar. Sci. Rep. 8 (1), 13723 (2018).
Deng, M. et al. The genus Cephonodes from Xisha islands, Hainan Province, China, with description of a new species Cephonodes sanshaensis Deng & Huang, 2023 sp. nov.(Lepidoptera: Sphingidae). J. Asia-Pac Biodivers. 17 (1), 117–124 (2024).
Sommung, B. & Hawkeswood, T. J. Cephonodes hylas (L., 1771) (Lepidoptera: Sphingidae) visiting flowers of Alpinia officinarum Hance (Zingiberaceae) in Thailand, with a review of some larval host plants and host flower records for C. hylas. Calodema 411, 1–7 (2016).
Bhagarathi, L. K. & Maharaj, G. Impact of climate change on insect biology, ecology, population dynamics, and pest management: A critical review. World J. Adv. Res. Rev. 19 (3), 541–568 (2023).
Subedi, B., Poudel, A. & Aryal, S. The impact of climate change on insect pest biology and ecology: Implications for pest management strategies, crop production, and food security. J. Agric. Food Res. 14, 100733 (2023).
Thuiller, W. Ecological niche modelling. Curr. Biol. 34 (6), R225–R229 (2024).
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17 (1), 43–57 (2011).
Hosseini, N., Ghorbanpour, M. & Mostafavi, H. Habitat potential modelling and the effect of climate change on the current and future distribution of three Thymus species in Iran using MaxEnt. Sci. Rep. 14 (1), 3641 (2024).
Wang, F., Yuan, X., Sun, Y. & Liu, Y. Species distribution modeling based on MaxEnt to inform biodiversity conservation in the Central Urban Area of Chongqing Municipality. Ecol. Indic. 158, 111491 (2024).
Astudillo, P. X. et al. Using surrogate species and MaxEnt modeling to prioritize areas for conservation of a páramo bird community in a tropical high Andean biosphere reserve. Arct. Antarct. Alp. Res. 56 (1), 2299362 (2024).
Lian, Y. et al. Spatio-temporal changes and habitats of rare and endangered species in Yunnan Province based on MaxEnt model. Land 13 (2), 240 (2024).
Kumar, R., Hajam, Y. A. & Kumar, I. & Neelam. Insect pollinators’s diversity in the Himalayan Region: Their role in agriculture and sustainable development. In Role of Science and Technology for Sustainable Future: Volume 1: Sustainable Development: A Primary Goal (243–276). (Springer, 2024).
Ballesteros-Mejia, L., Kitching, I. J. & Beck, J. Projecting the potential invasion of the Pink Spotted Hawkmoth (Agrius cingulata) across Africa. Int. J. Pest Manag. 57 (2), 153–159 (2011).
Hundsdoerfer, A. K., Mende, M. B., Kitching, I. J. & Cordellier, M. Taxonomy, phylogeography and climate relations of the Western Palaearctic spurge hawkmoth (Lepidoptera, Sphingidae, Macroglossinae). Zool. Scr. 40 (4), 403–417 (2011).
Keefe, H. E. & Kharouba, H. M. Growing degree-days do not explain moth species’ distributions at broad scales. Ecosphere 15 (7), e4885 (2024).
Gómez-Undiano, I. et al. Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models. Sci. Rep. 12 (1), 16234 (2022).
Kumar, S., Neven, L. G., Zhu, H. & Zhang, R. Assessing the global risk of establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt niche models. J. Econ. Entomol. 108 (4), 1708–1719 (2015).
Wang, Y. Q. et al. The distribution of Athetis lepigone and prediction of its potential distribution based on GARP and MaxEnt. J. Appl. Entomol. 141 (6), 431–440 (2017).
Jiang, D., Chen, S., Hao, M., Fu, J. & Ding, F. Mapping the potential global codling moth (Cydia pomonella L.) distribution based on a machine learning method. Sci. Rep. 8 (1), 13093 (2018).
Srivastava, V., Griess, V. C. & Keena, M. A. Assessing the potential distribution of Asian gypsy moth in Canada: A comparison of two methodological approaches. Sci. Rep. 10 (1), 22 (2020).
Yang, M., Wang, Y., Ding, W., Li, H. & Zhang, A. Predicting habitat suitability for the soybean pod borer Leguminivora glycinivorella (Matsumura) using optimized MaxEnt models with multiple variables. J. Econ. Entomol. 117 (5), 1796–1808 (2024).
Song, J. W. et al. Spatial ensemble modeling for predicting the potential distribution of Lymantria dispar asiatica (Lepidoptera: Erebidae: Lymantriinae) in South Korea. Environ. Monit. Assess. 194 (12), 889 (2022).
Timilsena, T. B. et al. Potential distribution of fall armyworm in Africa and beyond, considering climate change and irrigation patterns. Sci. Rep. 12 (1), 539 (2022).
Yang, M. et al. Predicting the potential global distribution of the plum fruit Moth grapholita funebrana treitscheke using ensemble models. Insects 15 (9) (2024).
Fekrat, L. & Farashi, A. Impacts of climatic changes on the worldwide potential geographical dispersal range of the leopard moth, Zeuzera pyrina (L.) (Lepidoptera: Cossidae). Glob Ecol. Conserv. 34, e02050 (2022).
Hartl, T., Srivastava, V., Prager, S. & Wist, T. Evaluating climate change scenarios on global pea aphid habitat suitability using species distribution models. Clim. Change Ecol. 7, 100084 (2024).
Sultana, S., Baumgartner, J. B., Dominiak, B. C., Royer, J. E. & Beaumont, L. J. Impacts of climate change on high priority fruit fly species in Australia. PloS one. 15 (2), e0213820 (2020).
Naranjo, S. E., Castle, S. J., De Barro, P. J. & Liu, S. S. Population dynamics, demography, dispersal and spread of Bemisia tabaci. In: (eds Stansly, P. & Naranjo, S.) Bemisia: Bionomics and Management of a Global Pest. Springer, Dordrecht (2010).
Sylla, S., Brévault, T., Monticelli, L. S., Diarra, K. & Desneux, N. Geographic variation of host preference by the invasive tomato leaf miner Tuta absoluta: implications for host range expansion. J. Pest Sci. 92, 1387–1396 (2019).
Dermauw, W., Pym, A., Bass, C., Van Leeuwen, T. & Feyereisen, R. Does host plant adaptation lead to pesticide resistance in generalist herbivores? Curr. Opin. Insect Sci. 26, 25–33 (2018).
Liebhold, A. M., Leonard, D., Marra, J. L. & Pfister, S. E. Area-wide management of invading gypsy moth (Lymantria dispar) populations in the USA. In: Area-wide Integrated Pest Management (551–560). (CRC, 2021).
Zhao, G. et al. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models. Ecol. Indic. 132, 108256 (2021).
Lake, T. A., Runquist, B., Moeller, D. A. & R. D., & Predicting range expansion of invasive species: Pitfalls and best practices for obtaining biologically realistic projections. Divers. Distrib. 26 (12), 1767–1779 (2020).
Wang, Y. et al. Prediction of the potentially suitable areas of Elymus dahuricus Turcz in China under climate change based on maxent. Sci. Rep. 15, 17959 (2025).
Author information
Authors and Affiliations
Contributions
K.O. performed the modelling, formulated the results, and wrote the final draft. T.P. helped in the formulation of results, figures, and the preparation of the final draft. A.C. analyzed data. S.U. provided charts and figures. S.K. conceived the idea of using machine learning, revised the final draft of the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1
Supplementary Material 2
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Omanakuttan, K., Pandey, T., Chettri, A. et al. Predicting the global distribution of Coffee Bee Hawk Moth (Cephanodes hylas L.) under climate change using MaxEnt.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-41791-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-41791-x
Keywords
- Ecological modelling
- MaxEnt
- Species distribution model
- Hawk Moth
Source: Ecology - nature.com

