Proceedings of the National Institute of Ecology of the Republic of Korea

National Institute of Ecology (국립생태원)
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Modeling Species Distributions to Predict Seasonal Habitat Range of Invasive Fish in the Urban Stream via Environmental DNA

  • Kang, Yujin (Department of Environmental Landscape Architecture, Graduate School of Environmental Studies, Seoul National University) ;
  • Shin, Wonhyeop (Integrated Major in Smart City Global Convergence, Seoul National University) ;
  • Yun, Jiweon (Department of Environmental Landscape Architecture, Graduate School of Environmental Studies, Seoul National University) ;
  • Kim, Yonghwan (Department of Environmental Landscape Architecture, Graduate School of Environmental Studies, Seoul National University) ;
  • Song, Youngkeun (Department of Environmental Landscape Architecture, Graduate School of Environmental Studies, Seoul National University)
  • Received : 2021.10.07
  • Accepted : 2021.12.14
  • Published : 2022.02.01
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Abstract

Species distribution models are a useful tool for predicting future distribution and establishing a preemptive response of invasive species. However, few studies considered the possibility of habitat for the aquatic organism and the number of target sites was relatively small compared to the area. Environmental DNA (eDNA) is the emerging tool as the methodology obtaining the bulk of species presence data with high detectability. Thus, this study applied eDNA survey results of Micropterus salmoides and Lepomis macrochirus to species distribution modeling by seasons in the Anyang stream network. Maximum Entropy (MaxEnt) model evaluated that both species extended potential distribution area in October compared to July from 89.1% (12,110,675 m2) to 99.3% (13,625,525 m2) for M. salmoides and 76.6% (10,407,350 m2) to 100% (13,724,225 m2) for L. macrochirus. The prediction value by streams was varied according to species and seasons. Also, models elucidate the significant environmental variables which affect the distribution by seasons and species. Our results identified the potential of eDNA methodology as a way to retrieve species data effectively and use data for building a model.

Keywords

Acknowledgement

This work was conducted with the support of the Korea Environment Industry & Technology Institute (KEITI) through its Urban Ecological Health Promotion Technology Development Project and funded by the Korea Ministry of Environment (MOE) (2019002760001). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」.

References

  1. Alexandre da Silva, M.V., Nunes Souza, J.V., de Souza, J.R.B., and Vieira, L.M. (2019). Modelling species distributions to predict areas at risk of invasion by the exotic aquatic New Zealand mudsnail Potamopyrgus antipodarum (Gray 1843). Freshwater Biology, 64, 1504-1518. https://doi.org/10.1111/fwb.13323
  2. Byers, J.E., McDowell, W.G., Dodd, S.R., Haynie, R.S., Pintor, L.M., and Wilde, S.B. (2013). Climate and pH predict the potential range of the invasive apple snail (Pomacea insularum) in the southeastern United States. PloS One, 8, e56812. https://doi.org/10.1371/journal.pone.0056812
  3. Byon, H., and Jeon, S. (1997). Feeding habit of Bluegill, Lepomis macrochirus Introduced in Korea. Korean Journal of Environmental Biology, 15, 165-174.
  4. Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E., and Yates, C.J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  5. Gallardo, B., Castro-Diez, P., Saldana-Lopez, A., and Alonso, A. (2020). Integrating climate, water chemistry and propagule pressure indicators into aquatic species distribution models. Ecological Indicators, 112, 106060. https://doi.org/10.1016/j.ecolind.2019.106060
  6. Griffiths, N.P., Bolland, J.D., Wright, R.M., Murphy, L.A., Donnelly, R.K., Watson, H.V., et al. (2020). Environmental DNA metabarcoding provides enhanced detection of the European eel Anguilla anguilla and fish community structure in pumped river catchments. Journal of Fish Biology, 97, 1375-1384. https://doi.org/10.1111/jfb.14497
  7. Harrel, R.C., Davis, B.J., and Dorris, T.C. (1967). Stream order and species diversity of fishes in an intermittent Oklahoma stream. American Midland Naturalist, 78, 428-436. https://doi.org/10.2307/2485240
  8. Horton, R.E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. GSA Bulletin, 56, 275-370. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2
  9. Hunter, M.E., Oyler-McCance, S.J., Dorazio, R.M., Fike, J.A., Smith, B.J., Hunter, C.T., et al. (2015). Environmental DNA (eDNA) sampling improves occurrence and detection estimates of invasive burmese pythons. PloS One, 10, e0121655. https://doi.org/10.1371/journal.pone.0121655
  10. Information of Korean Alien Species. (2021). Micropterus salmoides. Retrieved December 30, 2021 from https://kias.nie.re.kr/home/for/for02002v.do?clsSno=20381&searchClsGbn=eco.
  11. Kang, J.G., and Kim, J.T. (2016). Experiment and assessment of ascending capability for management of exotic fish species. Journal of the Korea Academia-Industrial Cooperation Society, 17, 265-278. https://doi.org/10.5762/KAIS.2016.17.9.265
  12. Kim, H.M., Kil, J.H., Lee, E.H., and An, K.G. (2013). Distribution characteristics of largemouth bass (Micropterus salmoides) as an exotic species, in some medium-to-large size Korean reservoirs and physico-chemical water quality in the habitats. Korean Journal of Ecology and Environment, 46, 541-550. https://doi.org/10.11614/KSL.2013.46.4.541
  13. Kim, J.E., and Lee, H.G. (2018). The evaluation of potential invasive species in the Gangneungnamdae Stream in Korea using a fish invasiveness screening kit. Korean Journal of Environmental Biology, 36, 73-81. https://doi.org/10.11626/KJEB.2018.36.1.073
  14. Lee, D.S., Lee, D.Y., Ji, C.W., Kwak, I.S., Hwang, S.J., Lee, H.J., et al. Impacts of introduced fishes (Carassius cuvieri, Micropterus salmoides, Lepomis macrochirus) on stream fish communities in South Korea. Korean Journal of Ecology and Environment, 53, 241-254. https://doi.org/10.11614/KSL.2020.53.3.241
  15. Lee, W.O., Yang, H., Yoon, S.W., and Park, J.Y. (2009). Study on the feeding habits of Micropterus salmoides in Lake Okjeong and Lake Yongdam, Korea. Korean Journal of Ichthyology, 21, 200-207.
  16. Li, Y., Li, M., Li, C., and Liu, Z. (2020). Optimized maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China. Forests, 11, 302. https://doi.org/10.3390/f11030302
  17. Mamun, M., Kim, S., and An, K.G. (2018). Distribution pattern prediction of an invasive alien species largemouth bass using a maximum entropy model (MaxEnt) in the Korean peninsula. Journal of Asia-Pacific Biodiversity, 11, 516-524. https://doi.org/10.1016/j.japb.2018.09.007
  18. Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J.Y., Sato, K., et al. (2015). MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. Royal Society Open Science, 2, 150088. https://doi.org/10.1098/rsos.150088
  19. Muha, T.P., Rodriguez-Rey, M., Rolla, M., and Tricarico, E. (2017). Using environmental DNA to improve species distribution models for freshwater invaders. Frontiers in Ecology and Evolution, 5, 158. https://doi.org/10.3389/fevo.2017.00158
  20. Muri, C.D., Handley, L.L., Bean, C.W., Li, J., Peirson, G., Sellers, G.S., et al. (2020). Read counts from environmental DNA (eDNA) metabarcoding reflect fish abundance and biomass in drained ponds. Metabarcoding and Metagenomics, 4, e56959. https://doi.org/10.3897/mbmg.4.56959
  21. Olds, B.P., Jerde, C.L., Renshaw, M.A., Li, Y., Evans, N.T., Turner, C.R., et al. (2016). Estimating species richness using environmental DNA. Ecology and Evolution, 6, 4214-4226. https://doi.org/10.1002/ece3.2186
  22. Phillips, S.B., Aneja, V.P., Kang, D., and Arya, S.P. (2006). Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. International Journal of Global Environmental, 6, 231-252.
  23. Phillips, S.J. (2017). A Brief Tutorial on Maxent. Retrieved December 30, 2021 from http://biodiversityinformatics.amnh.org/open_source/maxent/.
  24. Riaz, M., Kuemmerlen, M., Wittwer, C., Cocchiararo, B., Khaliq, I., Pfenninger, M., et al. (2020). Combining environmental DNA and species distribution modeling to evaluate reintroduction success of a freshwater fish. Ecological Applications, 30, e02034.
  25. Sato, Y., Miya, M., Fukunaga, T., Sado, T., and Iwasaki, W. (2018). MitoFish and MiFish pipeline: a mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Molecular Biology and Evolution, 35, 1553-1555. https://doi.org/10.1093/molbev/msy074
  26. Silva Neto, J.G.D., Sutton, W.B., Spear, S.F., Freake, M.J., Kery, M., and Schmidt, B.R. (2020). Integrating species distribution and occupancy modeling to study hellbender (Cryptobranchus alleganiensis) occurrence based on eDNA surveys. Biological Conservation, 251, 108787. https://doi.org/10.1016/j.biocon.2020.108787
  27. Son, M., and Byun, J. (2019). An experimental study on the habitat characteristics of largemouth bass. Journal of Korea Water Resources Association, 52, 845-853. https://doi.org/10.3741/JKWRA.2019.52.S-2.845
  28. Strahler, A.N. (1957). Quantitative analysis of watershed geomorphology. Eos, Transactions American Geophysical Union, 38, 913-920. https://doi.org/10.1029/TR038i006p00913
  29. Stuber R.J., Gebhart, G., and Maughan, O.E. (1982a). Habitat Suitability Index Models: Largemouth Bass. Washington, D.C.: U. S. Department of Interior, Fish and Wildlife Service.
  30. Stuber R.J., Gebhart, G., and Maughan, O.E. (1982b). Habitat Suitability Index Models: Bluegill. Washington, D.C.: U. S. Department of Interior, Fish and Wildlife Service.
  31. Tikhonov, G., Abrego, N., Dunson, D., and Ovaskainen, O. (2017). Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context. Methods in Ecology and Evolution, 8, 443-452. https://doi.org/10.1111/2041-210X.12723
  32. Ushio, M., Murata, K., Sado, T., Nishiumi, I., Takeshita, M., Iwasaki, W., et al. (2018). Demonstration of the potential of environmental DNA as a tool for the detection of avian species. Scientific Reports, 8, 4493. https://doi.org/10.1038/s41598-018-22817-5
  33. Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., and Cushing, C.E. (1980). The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37, 130-137. https://doi.org/10.1139/f80-017
  34. Vorste, R., McElmurray, P., Bell, S., Eliason, K.M., and Brown, B. (2017). Does stream size really explain biodiversity patterns in lotic systems? A call for mechanistic explanations. Diversity, 9, 26. https://doi.org/10.3390/d9030026
  35. Yang, X.Q., Kushwaha, S.P.S., Saran, S., Xu, J., and Roy, P.S. (2013). Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51, 83-87. https://doi.org/10.1016/j.ecoleng.2012.12.004
  36. Zhang, H., Yoshizawa, S., Iwasaki, W., and Xian, W. (2019). Seasonal fish assemblage structure using environmental DNA in the Yangtze Estuary and its adjacent waters. Frontiers in Marine Science, 6, 515. https://doi.org/10.3389/fmars.2019.00515