Abstract
The spongy moth, Lymantria dispar, is a pest that damages various tree species throughout North America and Eurasia, has recently emerged in South Korea, threatening local forests and landscapes. The establishment of effective countermeasures against this species’ outbreak requires predicting its potential distribution with climate change. In this study, we used species distribution models (CLIMEX and MaxEnt) to predict the potential distribution of the spongy moth and identify areas at risk of exposure to a sustained occurrence of the pest by constructing an ensemble map that simultaneously projected the outcomes of the two models. The results showed that the spongy moth could be distributed over the entire country under the current climate, but the number of suitable areas would decrease under a climate change scenario. This study is expected to provide basic data that can predict areas requiring intensive control and monitoring in advance with methodologically improved modeling technique.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material
Not applicable.
Code availability
Not applicable.
References
Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Ananko, G. G., & Kolosov, A. V. (2021). Asian gypsy moth (Lymantria dispar L.) populations: Tolerance of eggs to extreme winter temperatures. Journal of Thermal Biology, 102, 103123. https://doi.org/10.1016/j.jtherbio.2021.103123
APHIS, U. (2016). Pest alert: Asian gypsy moth. Agriculture, USDo (Ed.).
Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012
Brown, J. L. (2014). SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution, 5(7), 694–700. https://doi.org/10.1111/2041-210X.12200
Byeon, D. H., Jung, S., & Lee, W. H. (2018). Review of CLIMEX and MaxEnt for studying species distribution in South Korea. Journal of Asia-Pacific Biodiversity, 11(3), 325–333. https://doi.org/10.1016/j.japb.2018.06.002
Byeon, D. H., Kim, S. H., Jung, J. M., Jung, S., Kim, K. H., & Lee, W. H. (2021). Climate-based ensemble modelling to evaluate the global distribution of Anoplophora glabripennis (Motschulsky). Agricultural and Forest Entomology, 23(4), 569–583. https://doi.org/10.1111/afe.12462
CABI (Centre for Agriculture and Bioscience International). (2021). Invasive species compendium. CAB International, Wallingford, UK, from www.cabi.org/isc/datasheet/31807.
Choi, W. I., Kim, E. S., Yun, S. J., Lim, J. H., & Kim, Y. E. (2021). Quantification of one-year gypsy moth defoliation extent in Wonju, Korea. Using Landsat Satellite Images. Forests, 12(5), 545. https://doi.org/10.3390/f12050545
D’Amico, V., & Elkinton, J. S. (1995). Rainfall effects on transmission of gypsy moth (Lepidoptera: Lymantriidae) nuclear polyhedrosis virus. Environmental Entomology, 24(5), 1144–1149. https://doi.org/10.1093/ee/24.5.1144
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Elkinton, J. S., & Liebhold, A. M. (1990). Population dynamics of gypsy moth in North America. Annual Review of Entomology, 35(1), 571–596. https://doi.org/10.1146/annurev.en.35.010190.003035
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
GBIF (Global biodiversity information facility). (2021). GBIF.org (28 April 2021) GBIF occurrence download, from https://doi.org/10.15468/dl.3mny9k
Gebrewahid, Y., Abrehe, S., Meresa, E., Eyasu, G., Abay, K., Gebreab, G., Kidanemariam, K., Adissu, G., Abreha G., & Darcha, G. (2020). Current and future predicting potential areas of Oxytenanthera abyssinica (A. Richard) using MaxEnt model under climate change in northern Ethiopia. Ecological Processes, 9(1), 1–15. https://doi.org/10.1186/s13717-019-0210-8
Gray, D. R., Ravlin, F. W., & Braine, J. A. (2001). Diapause in the gypsy moth: A model of inhibition and development. Journal of Insect Physiology, 47(2), 173–184. https://doi.org/10.1016/S0022-1910(00)00103-7
Gray, D. R. (2010). Hitchhikers on trade routes: A phenology model estimates the probabilities of gypsy moth introduction and establishment. Ecological Applications, 20(8), 2300–2309. https://doi.org/10.1890/09-1540.1
Hajek, A. E., Elkinton, J. S., & Witcosky, J. J. (1996). Introduction and spread of the fungal pathogen Entomophaga maimaiga (Zygomycetes: Entomophthorales) along the leading edge of gypsy moth (Lepidoptera: Lymantriidae) spread. Environmental Entomology, 25(5), 1235–1247. https://doi.org/10.1093/ee/25.5.1235
Haynes, K. J., Bjørnstad, O. N., Allstadt, A. J., & Liebhold, A. M. (2013). Geographical variation in the spatial synchrony of a forest-defoliating insect: Isolation of environmental and spatial drivers. Proceedings of the Royal Society b: Biological Sciences, 280(1753), 20122373. https://doi.org/10.1098/rspb.2012.2373
Inoue, M. N., Suzuki-Ohno, Y., Haga, Y., Aarai, H., Sano, T., Martemyanov, V. V., & Kunimi, Y. (2019). Population dynamics and geographical distribution of the gypsy moth, Lymantria dispar, in Japan. Forest Ecology and Management, 434, 154–164. https://doi.org/10.1016/j.foreco.2018.12.022
Iwaizumi, R., Arakawa, K., & Koshio, C. (2010). Nocturnal flight activities of the female Asian gypsy moth, Lymantria dispar (Linnaeus)(Lepidoptera: Lymantriidae). Applied Entomology and Zoology, 45(1), 121–128. https://doi.org/10.1303/aez.2010.121
Jung, J. M., Lee, W. H., & Jung, S. (2016). Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomological Research, 46(4), 223–235. https://doi.org/10.1111/1748-5967.12171
Jung, J. M., Lee, S. G., Kim, K. H., Jeon, S. W., Jung, S., & Lee, W. H. (2019). The potential distribution of the potato tuber moth (Phthorimaea Operculella) based on climate and host availability of potato. Agronomy, 10(1), 12. https://doi.org/10.3390/agronomy10010012
Kriticos, D. J., Webber, B. L., Leriche, A., Ota, N., Macadam, I., Bathols, J., & Scott, J. K. (2012). CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution, 3(1), 53–64. https://doi.org/10.1111/j.2041-210X.2011.00134.x
Kriticos, D. J., Maywald, G. F., Yonow, T., Zurcher, E. J., Herrmann, N. I., & Sutherst, R. (2015). Exploring the effects of climate on plants, animals and diseases. CLIMEX Version, 4, 184.
Kumar, S., Neven, L. G., Zhu, H., & Zhang, R. (2015). Assessing the global risk of establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt niche models. Journal of Economic Entomology, 108(4), 1708–1719. https://doi.org/10.1093/jee/tov166
Leonard, D. E. (1974). Recent developments in ecology and control of the gypsy moth. Annual Review of Entomology, 19(1), 197–229.
Lobo, J. M., Jiménez-Valverde, A., & Real, R. (2008). AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17(2), 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Liebhold, A. M. (1995). Suitability of North American tree species to the gypsy moth: A summary of field and laboratory tests (Vol. 211). US Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.
Limbu, S., Keena, M., Chen, F., Cook, G., Nadel, H., & Hoover, K. (2017). Effects of temperature on development of Lymantria dispar asiatica and Lymantria dispar japonica (Lepidoptera: Erebidae). Environmental Entomology, 46(4), 1012–1023. https://doi.org/10.1093/ee/nvx111
Liu, C., Newell, G., & White, M. (2016). On the selection of thresholds for predicting species occurrence with presence-only data. Ecology and Evolution, 6(1), 337–348. https://doi.org/10.1002/ece3.1878
Matsuki, M., Kay, M., Serin, J., Floyd, R., & Scott, J. K. (2001). Potential risk of accidental introduction of Asian gypsy moth (Lymantria dispar) to Australasia: Effects of climatic conditions and suitability of native plants. Agricultural and Forest Entomology, 3(4), 305–320. https://doi.org/10.1046/j.1461-9555.2001.00119.x
Miller, J. (2010). Species distribution modeling. Geography. Compass, 4(6), 490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5(11), 1198–1205. https://doi.org/10.1111/2041-210X.12261
Paini, D. R., Mwebaze, P., Kuhnert, P. M., & Kriticos, D. J. (2018). Global establishment threat from a major forest pest via international shipping: Lymantria dispar. Scientific Reports, 8(1), 1–7. https://doi.org/10.1038/s41598-018-31871-y
Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213(1), 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pogue, M., & Schaefer, P. W. (2007). A review of selected species of Lymantria Hübner (1819)(Lepidoptera: Noctuidae: Lymantriinae) from subtropical and temperate regions of Asia, including the descriptions of three new species, some potentially invasive to North America. U.S. Dept. of Agriculture, Forest Health Technology Enterprise Team, Washington, D.C. (2007).
Reineke, A., Karlovsky, P., & Zebitz, C. P. W. (1999). Amplified fragment length polymorphism analysis of different geographic populations of the gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae). Bulletin of Entomological Research, 89(1), 79–88. https://doi.org/10.1017/S0007485399000103
Shabani, F., Kumar, L., & Ahmadi, M. (2016). A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecology and Evolution, 6(16), 5973–5986. https://doi.org/10.1002/ece3.2332
Syfert, M. M., Smith, M. J., & Coomes, D. A. (2013). The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE, 8(2), e55158. https://doi.org/10.1371/journal.pone.0055158
Thapa, A., Wu, R., Hu, Y., Nie, Y., Singh, P. B., Khatiwada, J. R., Yan, L., Gu, X., & Wei, F. (2018). Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution, 8(21), 10542–10554. https://doi.org/10.1002/ece3.4526
Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD–A platform for ensemble forecasting of species distributions. Ecography, 32(3), 369–373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
Tuthill, R. W., Canada, A. T., Wilcock, K., Etkind, P. H., O’Dell, T. M., & Shama, S. K. (1984). An epidemiologic study of gypsy moth rash. American Journal of Public Health, 74(8), 799–803. https://doi.org/10.2105/AJPH.74.8.799
Weseloh, R. M., Andreadis, T. G., Onstad D. W. (1993). Modeling the influence of rainfall and temperature on the phenology of infection of gypsy moth Lymantria dispar larvae by the fungus Entomophaga maimaiga. Biological Control, 3(4), 311–318. S1049964483710406. https://doi.org/10.1006/bcon.1993.1040
Funding
This study was supported by the “Investigation of larval occurrence characteristics of gypsy moths (Project no. FE0702-2021–01-2021)” funded by the National Institute of Forest Science in Korea.
Author information
Authors and Affiliations
Contributions
Conceptualization, J.-W. Song, and W.-H. Lee; methodology, J.-W. Song, D.E. Kim, H. Lee, and W.-H. Lee; software, J.-W. Song; formal analysis, J.-W. Song; resources, D.E. Kim, and H. Lee; writing—original draft preparation, J.-W. Song, and W.-H. Lee; writing—review and editing, J.-W. Song, S. Jung, and W.-H. Lee; visualization, J.-W. Song; supervision, S. Jung, and W.-H. Lee; funding acquisition, S. Jung.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent for publication
Consent to publish.
Conflict of interest
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.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Song, JW., Jung, JM., Nam, Y. et al. Spatial ensemble modeling for predicting the potential distribution of Lymantria dispar asiatica (Lepidoptera: Erebidae: Lymantriinae) in South Korea. Environ Monit Assess 194, 889 (2022). https://doi.org/10.1007/s10661-022-10609-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-022-10609-4