Giovanelli, J. G. R., Haddad, C. F. B. & Alexandrino, J. Predicting the potential distribution of the alien invasive American bullfrog (Lithobates catesbeianus) in Brazil. Biol. Invas. 10, 585–590. https://doi.org/10.1007/s10530-007-9154-5 (2008).
Google Scholar
Sillero, N. Modelling suitable areas for Hyla meridionalis under current and future hypothetical expansion scenarios. Amphib. Reptil. 31, 37–50. https://doi.org/10.1163/156853810790457948 (2010).
Google Scholar
Foley, D. H. et al. Geographic distribution, evolution, and disease importance of species within the Neotropical Anopheles albitarsis Group (Diptera, Culicidae). J. Vector Ecol. 39, 168–181. https://doi.org/10.1111/j.1948-7134.2014.12084.x,Pubmed:24820570 (2014).
Google Scholar
Brito, J. C. et al. Biogeography and conservation of viperids from North-West Africa: An application of ecological niche-based models and GIS. J. Arid Environ. 75, 1029–1037. https://doi.org/10.1016/j.jaridenv.2011.06.006 (2011).
Google Scholar
Kim, J., Seo, C., Kwon, H., Ryu, J. & Kim, M. A study on the species distribution modeling using national ecosystem survey data. J. Environ. Impact Assess. 21, 593–607 (2012) (in Korean with English abstract).
Brown, J. L. et al. Spatial biodiversity patterns of Madagascar’s amphibians and reptiles. PLoS One 11, e0144076. https://doi.org/10.1371/journal.pone.0144076,Pubmed:26735688 (2016).
Google Scholar
Do, M. S. et al. Spatial distribution patterns and prediction of hotspot area for endangered herpetofauna species in Korea. Korean J. Environ. Ecol. 31, 381–396. https://doi.org/10.13047/KJEE.2017.31.4.381 (2017).
Google Scholar
Ficetola, G. F., Thuiller, W. & Padoa-Schioppa, E. From introduction to the establishment of alien species: bioclimatic differences between presence and reproduction localities in the slider turtle. Divers. Distrib. 15, 108–116. https://doi.org/10.1111/j.1472-4642.2008.00516.x (2009).
Google Scholar
Sillero, N. Modelling a species in expansion at local scale: Is Hyla meridionalis colonising new areas in Salamanca, Spain. Acta Herpetol. 4, 37–46 (2009).
Yun, S., Lee, J. W. & Yoo, J. C. Host-parasite interaction augments climate change effect in an avian brood parasite, the lesser cuckoo Cuculus poliocephalus. Glob. Ecol. Conserv. 22, e00976. https://doi.org/10.1016/j.gecco.2020.e00976 (2020).
Google Scholar
Katayama, N., Amano, T., Fujita, G. & Higuchi, H. Spatial overlap between the intermediate egret Egretta intermedia and its aquatic prey at two spatiotemporal scales in a rice paddy landscape. Zool. Stud. 51, 1105–1112 (2012).
Katayama, N. et al. Indirect positive effects of agricultural modernization on the abundance of Japanese tree frog tadpoles in rice fields through the release from predators. Aquat. Ecol. 47, 225–234. https://doi.org/10.1007/s10452-013-9437-0 (2013).
Google Scholar
Valencia-Aguilar, A., Cortés-Gómez, A. M. & Ruiz-Agudelo, C. A. Ecosystem services provided by amphibians and reptiles in Neotropical ecosystems. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 9, 257–272. https://doi.org/10.1080/21513732.2013.821168 (2013).
Google Scholar
Cortes, A. M., Ruiz-Agudelo, C. A., Valencia-Aguilar, A. & Ladle, R. J. Ecological functions of Neotropical amphibians and reptiles: A review. Univ. Sci. 20, 229–245. https://doi.org/10.11144/Javeriana.SC20-2.efna (2015).
Google Scholar
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100 (2006).
Google Scholar
Hoffmann, A. A. & Sgró, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485. https://doi.org/10.1038/nature09670,Pubmed:21350480 (2011).
Google Scholar
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899. https://doi.org/10.1126/science.1184695,Pubmed:20466932 (2010).
Google Scholar
Penman, T. D., Pike, D. A., Webb, J. K. & Shine, R. Predicting the impact of climate change on Australia’s most endangered snake, Hoplocephalus bungaroides. Divers. Distrib. 16, 109–118. https://doi.org/10.1111/j.1472-4642.2009.00619.x (2010).
Google Scholar
Blank, L. & Blaustein, L. Using ecology niche modeling to predict the distributions of two endangered amphibian species in aquatic breeding sites. Hydrobiologia 693, 157–167. https://doi.org/10.1007/s10750-012-1101-5 (2012).
Google Scholar
de Pous, P., Beukema, W., Weterings, M., Dümmer, I. & Geniez, P. Area prioritization and performance evaluation of the conservation area network for the Moroccan herpetofauna: A preliminary assessment. Biodivers. Conserv. 20, 89–118. https://doi.org/10.1007/s10531-010-9948-0 (2011).
Google Scholar
NIBR (National Institute of Biological Resources). National List of Species (Reptiles and amphibians). https://www.kbr.go.kr/stat/ktsnfiledown/downpopup.do (2020).
Ministry of the Environment. List of Prohibited Wildlife Such as Capture and Harvesting (Ministry of the Environment, 2015).
NIBR (National Institute of Biological Resources). Red Data Book of Republic of Korea. Amphibians and Reptiles (NIBR, Incheon), 110–117 (2019).
Kim, J. B. Taxonomic list and distribution of Korean Amphibians. Korean J. Herpetol. 1, 1–13 (2009) (in Korean with English abstract).
Song, J. Y. & Lee, I. Elevation distribution of Korean Amphibians. Korean J. Herpetol. 1, 15–19 (2009) (in Korean with English abstract).
Jang, H. J. & Suh, J. H. Distribution of Amphibian species in South Korea. Korean J. Herpetol. 2, 45–51 (2010) (in Korean with English abstract).
Do, M. S. et al. Anuran Community Patterns in the rice fields of the mid-western region of the Republic of Korea. Glob. Ecol. Conserv. 26, e01448. https://doi.org/10.1016/j.gecco.2020.e01448 (2021).
Google Scholar
Kim, I. H., Son, S. H., Kang, S. W. & Kim, J. B. Distribution and habitat characteristics of the endangered Suweon-tree frog (Hyla suweonensis). Korean J. Herpetol. 4, 15–22 (2012) (in Korean with English abstract).
Do, M. S., Lee, J. W., Jang, H. J., Kim, D. I. & Yoo, J. C. Interspecific competition and spatial ecology of three species of vipers in Korea: An application of ecological niche-based models and GIS1a. Korean J. Environ. Ecol. 30, 173–184. https://doi.org/10.13047/KJEE.2016.30.2.173 (2016) (in Korean with English abstract).
Google Scholar
Do, M. S. et al. The study on habitat analysis and ecological niche of Korean Brown Frogs (Rana dybowskii, R. Coreana and R. huanrensis) using the species distribution model. Korean J. Herpetol. 9, 1–11 (2018).
Do, M. S., Choi, S., Jang, H. J. & Suh, J. H. Predicting the Distribution of three Korean pit viper Species (Gloydius brevicaudus, G. ussuriensis and G. intermedius) under Climate Change. Russ. J. Herpetol. (2022)
Koo, K. S., Park, D. & Oh, H. S. Analyzing habitat characteristics and predicting present and future suitable habitats of Sibynophis chinensis based on a climate change scenario. J. Asia Pac. Biodivers. 12, 1–6. https://doi.org/10.1016/j.japb.2018.11.001 (2019).
Google Scholar
Kim, H. W., Adhikari, P., Chang, M. H. & Seo, C. Potential distribution of amphibians with different habitat characteristics in response to climate change in South Korea. Animals (Basel) 11, 2185. https://doi.org/10.3390/ani11082185 (2021).
Google Scholar
Shin, Y. et al. How threatened is Scincella huanrenensis? An update on threats and trends. Conservation 1, 58–72. https://doi.org/10.3390/conservation1010005 (2021).
Google Scholar
Lee, S. Y. et al. Distribution prediction of Korean Clawed Salamander (Onychodactylus koreanus) according to the climate change. Korean J. Environ. Ecol. 35, 480–489. https://doi.org/10.13047/KJEE.2021.35.5.480 (2021).
Google Scholar
Ra, N. Y. Habitat and Behavioral Characteristics, Captive Breeding and Recovery Strategy of the Endangered Gold-Spotted Pond Frog (Rana Plancyi Chosenica). PhD thesis (Kangwon Natl Univ., 2010).
Borzée, A., Kim, J. Y. & Jang, Y. Asymmetric competition over calling sites in two closely related treefrog species. Sci. Rep. 6, 32569. https://doi.org/10.1038/srep32569,Pubmed:27599461 (2016).
Google Scholar
Song, W. Habitat analysis of Hyla suweonensis in the breeding season using species distribution modeling. J. Korean Environ. Res. Tech. 18, 71–82 (2015) (in Korean with English abstract).
Ahn, J. Y., Choi, S., Kim, H., Suh, J. H. & Do, M. S. Ecological niche and interspecific competition of two frog species (Pelophylax nigromaculatus and P. chosenicus) in South Korea using the geographic information system. KJEE 54, 363–373 (2021).
Google Scholar
Lee, J. H., Jang, H. J. & Suh, J. H. Ecological Guide Book of Herpetofauna in Korea (NIER, 2011) (in Korean).
Lee, J. H. & Park, D. Spatial ecology of translocated and resident Amur ratsnakes (Elaphe schrenckii) in two mountain valleys of South Korea. Asian Herpetol. Res. 2, 223–229 (2012).
Google Scholar
Do, M. S., Nam, K. B. & Yoo, J. C. First observation on courtship behavior of short-tailed viper snake, Gloydius saxatilis (Squamata: Viperidae) in Korea. J. Asia Pac. Biodivers. 10, 583–586. https://doi.org/10.1016/j.japb.2017.08.003 (2017).
Google Scholar
Do, M. S. & Nam, K. B. Distribution patterns and ecological niches of the red-tongued pit viper (Gloydius ussuriensis) and the Central Asian pit viper (Gloydius intermedius) in Cheonmasan Mountain, South Korea. Russ. J. Herpetol. 28, 348–354. https://doi.org/10.30906/1026-2296-2021-28-6-348-354 (2021).
Google Scholar
Do, M. S. Habitat use and hiding behavior of Central Asian pit viper (Gloydius intermedius). Korean J. Herpetol. 12, 1–8 (2021).
Min, M. S. et al. Discovery of the first Asian plethodontid salamander. Nature 435, 87–90. https://doi.org/10.1038/nature03474,Pubmed:15875021 (2005).
Google Scholar
Song, J. Y. Current status and distribution of reptiles in the Republic of Korea. Korean J. Environ. Biol. 25, 124–138 (2007).
Jang, H. J., Kim, D. I. & Jang, M. H. Distribution of reptiles in South Korea: based on the 3rd National Ecosystem Survey. Korean J. Herpetol. 7, 30–35 (2016) (in Korean with English abstract).
Seo, C. W., Choi, T. Y., Choi, Y. S. & Kim, D. Y. A study on wildlife habitat suitability modeling for goral (Nemorhaedus caudatus raddeanus) in Seoraksan national park. J. Korean Environ. Res. Reveg Tech. 11, 28–38 (2008) (in Korean with English abstract).
Kown, H. S. Integrated Evaluation Model of Biodiversity for Conservation Planning: Focused on MT, PhD thesis (Mt Deokyu and MT: Jiri, 2011, 2011). Gaya Regions (Graduate School, Seoul Natl Univ., 2011).
Urbina-Cardona, J. N. & Loyola, R. D. Applying niche-based models to predict endangered-hylid potential distributions: Are Neotropical protected areas effective enough?. Trop. Conserv. Sci. 1, 417–445. https://doi.org/10.1177/194008290800100408 (2008).
Google Scholar
Korea Forest Service. Forest area by administrative district. https://www.forest.go.kr/kfsweb/cop/bbs/selectBoardList.do?mn=NKFS_04_05_10&pageIndex=1&pageUnit=10&searchtitle=title&searchcont=&searchkey=&searchwriter=&searchdept=&searchWrd=&ctgryLrcls=CTGRY070&ntcStartDt=&ntcEndDt=&bbsId=BBSMSTR_1016 (2015).
Statistics Korea. Population and housing census results in South Korea. https://www.kostat.go.kr/portal/korea/kor_nw/1/2/2/index.board (2020).
Hyun, J. Brokering science, blaming culture: The US–South Korea ecological survey in the Demilitarized Zone, 1963–8. Hist. Sci. 59, 315–343. https://doi.org/10.1177/0073275320974209,Pubmed:33287575 (2021).
Google Scholar
Choung, E. H. A theoretical study on the landscape of the Korean DMZ and its spatial significance. Inter-Asian Cult. Stud. 22, 16–35. https://doi.org/10.1080/14649373.2021.1886465 (2021).
Google Scholar
Ministry of the Environment. Report on Biodiversity in the DMZ (Demilitarized Zone) Area. Seocheon-Gun (Ministry of the Environment, 2016).
Statistics Korea. Status of species investigation by national park in South Korea. https://kosis.kr/statHtml/statHtml.do?orgId=355&tblId=TX_35501_A069&conn_path=I3 (2021).
Koo, K. S., Kwon, S., Do, M. S. & Kim, S. Distribution characteristics of exotic turtles in Korean wild-Based. Korean J Ecol. Environ. 50, 286–294. https://doi.org/10.11614/KSL.2017.50.3.286 (2017).
Google Scholar
National Institute of Ecology. 30 Years of the Natural Environment Survey 1986–2015 (National Inst. of Ecology, Seocheon, 2017).
Korea National Park Research Institute. Report on Natural Resource Study. https://www.knps.or.kr/ (2021).
GBIF. Global Biodiversity Information Facility Home. http://www.gbif.org/ (2020).
Kim, D. I. Species Distribution Modeling, Microhabitat Use, and Morphological Variation of the Schlegel’s Japanese Gecko (Gekko japonicus). PhD thesis (Graduate School, Kangwon Natl Univ., 2019).
Borzée, A. et al. Yellow Sea mediated segregation between North East Asian Dryophytes species. PLoS One 15, e0234299. https://doi.org/10.1371/journal.pone.0234299,Pubmed:32579561 (2020).
Google Scholar
NGII (National Geographic Information Institute). Digital Topographic Map. https://www.ngii.go.kr (2013).
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).
Google Scholar
Pradhan, P. Strengthening MaxEnt modelling through screening of redundant explanatory bioclimatic variables with variance inflation factor analysis. Researcher 8, 29–34 (2016).
Yi, Y. J., Cheng, X., Yang, Z. F. & Zhang, S. H. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecol. Eng. 92, 260–269. https://doi.org/10.1016/j.ecoleng.2016.04.010 (2016).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2013).
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).
Google Scholar
Phillips, S., Dudik, M. & Schapire, R. A maximum entropy approach to species distribution modeling. In Proceeding of the 21st International Conference on Machine Learning 655–662 (ACM Pr., 2004).
Marchessaux, G., Lüskow, F., Sarà, G. & Pakhomov, E. A. Predicting the current and future global distribution of the invasive freshwater hydrozoan Craspedacusta sowerbii. Sci. Rep. 11, 23099. https://doi.org/10.1038/s41598-021-02525-3 (2021).
Google Scholar
VanderWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?. Ecol. Modell. 220, 589–594. https://doi.org/10.1016/j.ecolmodel.2008.11.010 (2009).
Google Scholar
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).
Google Scholar
Yaworsky, P. M., Vernon, K. B., Spangler, J. D., Brewer, S. C. & Codding, B. F. Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument. PLoS One 15, e0239424. https://doi.org/10.1371/journal.pone.0239424,Pubmed:33002016 (2020).
Google Scholar
Harte, J. Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics (OUP, 2011).
Google Scholar
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x (2006).
Google Scholar
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).
Google Scholar
Zacarias, D. & Loyola, R. Climate change impacts on the distribution of venomous snakes and snakebite risk in Mozambique. Clim. Change 152, 195–207. https://doi.org/10.1007/s10584-018-2338-4 (2019).
Google Scholar
del Castillo Domínguez, S. L. et al. Predicting the invasion of the acoustic niche: potential distribution and call transmission efficiency of a newly introduced frog in Cuba. Perspect. Ecol. Conserv. 19, 90–97. https://doi.org/10.1016/j.pecon.2020.12.002 (2021).
Google Scholar
Lee, J. W. et al. Spatial patterns, ecological niches, and interspecific competition of avian brood parasites: Inferring from a case study of Korea. Ecol. Evol. 4, 3689–3702. https://doi.org/10.1002/ece3.1209,Pubmed:25478158 (2014).
Google Scholar
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393. https://doi.org/10.1111/j.0906-7590.2005.03957.x (2005).
Google Scholar
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643. https://doi.org/10.1111/jbi.12227 (2014).
Google Scholar
Segal, R. D., Massaro, M., Carlile, N. & Whitsed, R. Small-scale species distribution model identifies restricted breeding habitat for an endemic island bird. Anim. Conserv. 24, 959–969. https://doi.org/10.1111/acv.12698 (2021).
Google Scholar
Mori, E. et al. How the South was won: Current and potential range expansion of the crested porcupine in Southern Italy. Mamm. Biol. 101, 11–19. https://doi.org/10.1007/s42991-020-00058-2 (2021).
Google Scholar
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293. https://doi.org/10.1126/science.3287615,Pubmed:3287615 (1988).
Google Scholar
Townsend Peterson, A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30, 550–560. https://doi.org/10.1111/j.0906-7590.2007.05102.x (2007).
Google Scholar
Jiménez-Valverde, A., Lobo, J. M. & Hortal, J. Not as good as they seem: The importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x (2008).
Google Scholar
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008).
Google Scholar
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008).
Google Scholar
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197. https://doi.org/10.1890/07-2153.1,Pubmed:19323182 (2009).
Google Scholar
Bosso, L. et al. Loss of potential bat habitat following a severe wildfire: A model-based rapid assessment. Int. J. Wildland Fire 27, 756–769. https://doi.org/10.1071/WF18072 (2018).
Google Scholar
Zhuang, H. et al. Optimized hot spot analysis for probability of species distribution under different spatial scales based on MaxEnt model: Manglietia insignis case. Biodivers. Sci. 26, 931–940. https://doi.org/10.17520/biods.2018059 (2018).
Google Scholar
NGII (National Geographic Information Institute). Geographical Extent of the Conservation Area in South Korea. https://www.ngii.go.kr (2021).
Bosso, L. et al. A gap analysis for threatened bat populations on Sardinia hystrix, the Italian. J. Mammal. 27, 212–214 (2016).
Ahmadi, M. et al. Species and space: A combined gap analysis to guide management planning of conservation areas. Landsc. Ecol. 35, 1505–1517. https://doi.org/10.1007/s10980-020-01033-5 (2020).
Google Scholar
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