IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p673-d1020220.html
   My bibliography  Save this article

Taxonomic and Functional Diversity of Benthic Macroinvertebrate Assemblages in Reservoirs of South Korea

Author

Listed:
  • Da-Yeong Lee

    (Department of Biology, College of Science, Kyung Hee University, Dongdaemun, Seoul 02447, Republic of Korea)

  • Dae-Seong Lee

    (Department of Biology, College of Science, Kyung Hee University, Dongdaemun, Seoul 02447, Republic of Korea)

  • Young-Seuk Park

    (Department of Biology, College of Science, Kyung Hee University, Dongdaemun, Seoul 02447, Republic of Korea)

Abstract

Numerous community indices have been developed to quantify the various aspects of communities. However, indices including functional aspects have been less focused on. Here, we examined how community composition varies in response to the environment and discovered the relationship between taxonomic diversity and functional diversity while considering the environment. Macroinvertebrate communities were collected from 20 reservoirs in South Korea. To characterize functional diversity, functional traits in four categories were considered: generation per year, adult lifespan, adult size, and functional feeding groups. Based on their community composition, we classified the reservoirs using hierarchical cluster analysis. Physicochemical and land use variables varied considerably between clusters. Non-metric multidimensional scaling indicated differences between reservoirs and clusters in terms of structure, functional diversity, and environmental variables. A self-organizing map was used to categorize functional traits, and network association analysis was used to unravel relationships between functional traits. Our results support the characteristics of species’ survival strategies such as r- and K-selection. Functional richness exhibited a relationship with taxonomic diversity. Our findings suggest that different types of diversity could play complementary roles in identifying biodiversity. Our findings should prove useful in developing new criteria for assessing freshwater ecosystem health, as well as in evaluating and predicting future alteration of benthic macroinvertebrate communities facing anthropogenic disturbances.

Suggested Citation

  • Da-Yeong Lee & Dae-Seong Lee & Young-Seuk Park, 2022. "Taxonomic and Functional Diversity of Benthic Macroinvertebrate Assemblages in Reservoirs of South Korea," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
  • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:673-:d:1020220
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/673/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/1/673/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Samraat Pawar & Anthony I. Dell & Van M. Savage, 2012. "Dimensionality of consumer search space drives trophic interaction strengths," Nature, Nature, vol. 486(7404), pages 485-489, June.
    2. Gergs, André & Ratte, Hans Toni, 2009. "Predicting functional response and size selectivity of juvenile Notonecta maculata foraging on Daphnia magna," Ecological Modelling, Elsevier, vol. 220(23), pages 3331-3341.
    3. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    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. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    2. Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    3. Yoichi Matsumoto, 2013. "Heterogeneous Combinations of Knowledge Elements: How the Knowledge Base Structure Impacts Knowledge-related Outcomes of a Firm," Discussion Paper Series DP2013-15, Research Institute for Economics & Business Administration, Kobe University.
    4. Man-, ZuyiKeunZuyi Wang & Takagi, Chifumi & Kim, Man-Keun & Chung, Anh, 2022. "Uncover Drivers Influencing Consumers' WTP Using Machine Learning: Case of Organic Coffee in Taiwan," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322150, Agricultural and Applied Economics Association.
    5. Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
    6. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.
    7. Małecka-Ziembińska Edyta & Siwiec Anna, 2020. "Searching for similarities in EU corporate income taxes for their harmonization," Economics and Business Review, Sciendo, vol. 6(4), pages 72-94, December.
    8. Nancy Awad & Jean-Francois Couchot & Bechara Al Bouna & Laurent Philippe, 2020. "Publishing Anonymized Set-Valued Data via Disassociation towards Analysis," Future Internet, MDPI, vol. 12(4), pages 1-21, April.
    9. Ranjan, Ravi & Bagchi, Sumanta, 2016. "Functional response and body size in consumer–resource interactions: Unimodality favors facilitation," Theoretical Population Biology, Elsevier, vol. 110(C), pages 25-35.
    10. Scholz, Michael, 2016. "R Package clickstream: Analyzing Clickstream Data with Markov Chains," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i04).
    11. Khanh Giang Le & Quang Hoc Tran & Van Manh Do, 2023. "Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques," Sustainability, MDPI, vol. 16(1), pages 1-19, December.
    12. Jasleen Kaur & Khushdeep Dharni, 2022. "Assessing efficacy of association rules for predicting global stock indices," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 329-339, September.
    13. Deszczyński, Bartosz & Beręsewicz, Maciej, 2021. "The maturity of relationship management and firm performance – A step toward relationship management middle-range theory," Journal of Business Research, Elsevier, vol. 135(C), pages 358-372.
    14. Matthieu Barbier & James R Watson, 2016. "The Spatial Dynamics of Predators and the Benefits and Costs of Sharing Information," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-22, October.
    15. Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.
    16. Ji Yeon Lee & Richa Kumari & Jae Yun Jeong & Tae-Hyun Kim & Byeong-Hee Lee, 2020. "Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
    17. Yoonju Lee & Heejin Kim & Hyesun Jeong & Yunhwan Noh, 2020. "Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
    18. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    19. Suelane Garcia Fontes & Ronaldo Gonçalves Morato & Silvio Luiz Stanzani & Pedro Luiz Pizzigatti Corrêa, 2021. "Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    20. Witting, Lars, 2017. "The natural selection of metabolism and mass selects allometric transitions from prokaryotes to mammals," Theoretical Population Biology, Elsevier, vol. 117(C), pages 23-42.

    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:jijerp:v:20:y:2022:i:1:p:673-:d:1020220. 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.