IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p634-d478313.html
   My bibliography  Save this article

Settlement and Recruitment Potential of Four Invasive and One Indigenous Barnacles in South Korea and Their Future

Author

Listed:
  • Michael Dadole Ubagan

    (Marine Biological Resources Institute, Sahmyook University, Seoul 01795, Korea
    Department of Animal Biotechnology and Resource, College of Science and Technology, Sahmyook University, Seoul 01795, Korea
    These authors contributed equally to this work.)

  • Yun-Sik Lee

    (Marine Biological Resources Institute, Sahmyook University, Seoul 01795, Korea
    O-Jeong Resilience Institute, Korea University, Seoul 02841, Korea
    These authors contributed equally to this work.)

  • Taekjun Lee

    (Marine Biological Resources Institute, Sahmyook University, Seoul 01795, Korea)

  • Jinsol Hong

    (Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Korea)

  • Il Hoi Kim

    (Marine Biological Resources Institute, Sahmyook University, Seoul 01795, Korea)

  • Sook Shin

    (Marine Biological Resources Institute, Sahmyook University, Seoul 01795, Korea
    O-Jeong Resilience Institute, Korea University, Seoul 02841, Korea
    These authors contributed equally to this work.)

Abstract

Invasion by nonindigenous species is a major threat to marine ecosystems. In this study, the distribution and occupied area (as a percentage) of four invasive barnacles ( Amphibalanus amphitrite , Amphibalanus eburneus , Amphibalanus improvisus , Perforatus perforatus ), and one indigenous ( Balanus trigonus ) barnacle in 13 ports in three Korean seas (East sea, Korea Strait, and Yellow Sea) were investigated. The average ratio for all five species was 11.17% in summer and 7.59% in winter, indicating a higher occupancy in summer. B. trigonus , which is an indigenous species, was found on all ports, except for one (IC). Of the invasive species, A. amphitrite was found mainly in the Yellow Sea, A. improvisus in the Korea Strait, and A. eburneus along with P. perforatus were found in the East Sea. From nonmetric multidimensional scaling (NMDS) analysis, six parameters related to water temperature and salinity were found to be significantly correlated with the distribution and occupancy status of these five barnacles. Using the six parameters as independent variables, random forest (RF) models were developed. Based on these models, the predicted future dominant invasive species were A. improvisus and A. amphitrite in the Yellow Sea and P. perforatus in the East Sea and Korea Strait. This study suggests that long-term monitoring of invasive species is crucial, and that determining the relationship between the results of monitoring and environmental variables can be helpful in predicting the damage caused by invasive species resulting from environmental changes.

Suggested Citation

  • Michael Dadole Ubagan & Yun-Sik Lee & Taekjun Lee & Jinsol Hong & Il Hoi Kim & Sook Shin, 2021. "Settlement and Recruitment Potential of Four Invasive and One Indigenous Barnacles in South Korea and Their Future," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:634-:d:478313
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/634/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/2/634/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    2. Weijun Wang & Dan Zhao & Liguo Fan & Yulong Jia, 2019. "Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine," Energies, MDPI, vol. 12(11), pages 1-21, June.
    3. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
    4. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    5. Zardad Khan & Asma Gul & Aris Perperoglou & Miftahuddin Miftahuddin & Osama Mahmoud & Werner Adler & Berthold Lausen, 2020. "Ensemble of optimal trees, random forest and random projection ensemble classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 97-116, March.
    6. Jin Li & Maggie Tran & Justy Siwabessy, 2016. "Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-29, February.
    7. Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A. & Crossman, Richard J. & Masegosa, Andrés R., 2014. "Classification with decision trees from a nonparametric predictive inference perspective," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 789-802.
    8. Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
    9. Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
    10. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.
    11. Hermel Homburger & Manuel K Schneider & Sandra Hilfiker & Andreas Lüscher, 2014. "Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
    12. Ingrida Vaiciulyte & Zivile Kalsyte & Leonidas Sakalauskas & Darius Plikynas, 2017. "Assessment of market reaction on the share performance on the basis of its visualization in 2D space," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 309-318, March.
    13. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
    14. Hapfelmeier, Alexander & Hornung, Roman & Haller, Bernhard, 2023. "Efficient permutation testing of variable importance measures by the example of random forests," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    15. Dogah, Kingsley E. & Premaratne, Gamini, 2018. "Sectoral exposure of financial markets to oil risk factors in BRICS countries," Energy Economics, Elsevier, vol. 76(C), pages 228-256.
    16. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
    17. Barbara Baranowska & Anna Kajdy & Paulina Pawlicka & Ernest Pokropek & Michał Rabijewski & Dorota Sys & Artur Pokropek, 2020. "What are the Critical Elements of Satisfaction and Experience in Labor and Childbirth—A Cross-Sectional Study," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    18. Massimiliano Fessina & Giambattista Albora & Andrea Tacchella & Andrea Zaccaria, 2022. "Which products activate a product? An explainable machine learning approach," Papers 2212.03094, arXiv.org.
    19. Edward Gage & David Cooper, 2015. "The Influence of Land Cover, Vertical Structure, and Socioeconomic Factors on Outdoor Water Use in a Western US City," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3877-3890, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:634-:d:478313. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.