Next Article in Journal
A Study on the Ice Resistance Characteristics of Ships in Rafted Ice Based on the Circumferential Crack Method
Previous Article in Journal
An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 6
10.3390/w16060853
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Advancing River Health Assessments: Integrating Microscopy and Molecular Techniques through Diatom Indices

by
Ha-Kyung Kim
1,2,
In-Hwan Cho
2,3,
Eun-A Hwang
2,4,
Byung-Hun Han
2 and
Baik-Ho Kim
2,5,*
1
Water Environmental Engineering Research Division, National Institute of Environmental Research, Inchon 22689, Republic of Korea
2
Department of Environmental Science, Hanyang University, Seoul 04763, Republic of Korea
3
Migang E&C Co., Ltd., Gyeonggi 14057, Republic of Korea
4
Han River Environment Research Center, National Institute of Environmental Research, Gyeonggi 12585, Republic of Korea
5
Department of Life Science and Research Institute for Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
*
Author to whom correspondence should be addressed.
Water 2024, 16(6), 853; https://doi.org/10.3390/w16060853
Submission received: 27 January 2024 / Revised: 21 February 2024 / Accepted: 12 March 2024 / Published: 15 March 2024
(This article belongs to the Section Urban Water Management)
Versions Notes

Abstract

:
This paper reviews the evolution and integration of diatom-based water quality assessments with environmental DNA (eDNA) techniques for advancing river ecosystem health evaluations. Traditional methods, relying on microscopy and diatom indices, have significantly contributed to our understanding of aquatic ecosystems but face challenges such as the need for taxonomic expertise and the labor-intensive nature of sample collection. Recent advancements in molecular biology, particularly eDNA analysis, offer opportunities to overcome these limitations, providing more accurate and comprehensive assessments. This study highlights the benefits of combining traditional microscopy with modern molecular techniques, enhancing the precision and efficiency of water quality evaluations. By addressing the challenges of standardizing methods and improving species identification through comprehensive reference libraries and advanced bioinformatics tools, this integrated approach aims to refine and advance the effectiveness of diatom-based strategies in monitoring and managing river health amidst environmental changes.

1. Introduction

Recent research has highlighted the severe challenges that rapid urbanization, industrialization, and economic growth pose to biodiversity and ecological balance. Studies have together underscored the grave consequences of these developments, including loss of biodiversity and deterioration of ecosystems [1,2,3,4,5,6,7]. This evidence has spurred global efforts to rejuvenate aquatic ecosystems, focusing mainly on addressing eutrophication and restoring biodiversity and ecological equilibrium [8,9,10].
The escalation of human activities, characterized by higher population densities and industrial activities, has resulted in an increase in pollutants discharged into aquatic environments, thereby endangering the health of river ecosystems. This situation highlights the urgent need for improved detection of hazardous substances and the establishment of stricter chemical pollutant standards [11,12,13,14,15]. Furthermore, the significance of biological indicators, such as fish, aquatic insects, periphyton, and aquatic plants, in the global evaluation of aquatic ecosystems has been recognized, emphasizing the necessity of maintaining ecological integrity in the management of aquatic ecosystems [16,17,18,19].
Specifically, diatoms have been identified as crucial indicators for assessing the ecological health of water bodies, providing essential insights into disturbances impacting river ecosystems [20,21,22,23]. Despite their importance, traditional methods for benthic diatom-based water quality assessment in river ecosystems are hampered by challenges including subjective interpretations, the need for specialized taxonomic expertise, and difficulties in processing samples. These issues mirror the technical obstacles encountered in environmental DNA (eDNA) analysis, such as contamination and taxonomic inconsistencies [24,25,26,27].
This study aims to improve the assessment of river ecosystem health by developing and applying rapid and accurate species-specific genetic methods and data collection protocols for diatoms. By comparing traditional diatom analysis techniques with those based on eDNA, this research intends to identify more effective strategies for monitoring and conserving aquatic environments.

2. The Evolution of Microscopy-Enabled Diatom-Based Water Quality Assessment

Since the late 20th century, the development of diatom-based assessments has played a pivotal role in evaluating the health of aquatic ecosystems and the overall quality of water worldwide. Diatom indices, as highlighted by researchers such as Kim et al. [28] and Masouras et al. [29], have significantly improved our capability to analyze diatom cluster data for assessing pollution tolerance, nutrient levels, trophic states, and the general ecological health of water bodies. These methods have been continually refined to enhance the health of aquatic ecosystems across the globe (Supplementary Materials S1).
A major breakthrough came in 1979 with the introduction of Descy’s Index, which initiated the quantitative assessment of water quality using diatom data. This breakthrough was followed by the establishment of several key indices, such as Bourelly’s Generic Diatom Index (1981), Foerster’s German Diatom Index (1982), the Diatom Assemblage Index (DAI) in 1986, and the Trophic Diatom Index (TDI) in 1995. These indices, taking into account ecological preferences and pollution tolerance, are utilized to assess the health of rivers and streams. In 2004, the European Committee for Standardization (CEN), along with updates to the Biological Diatom Index (BDI) in 1996 and 2009, standardized ecological assessment methods using diatom indicators. Moreover, the launch of the Index of Biotic Integrity (IBI) in 2005 and the Acid Mine Drainage-Diatom Index (AMD-DIBI) in 2010 further enhanced assessment capabilities by incorporating multiple indicators and accounting for specific environmental conditions.
From 1979 to 2019, the creation of numerous diatom indices has been critical for evaluating and monitoring aquatic ecosystems globally. Nevertheless, the application of diatom-based assessments encounters challenges such as regional variations in diatom species composition and sensitivity, necessitating the development of region-specific indices. Accurate species identification demands taxonomic expertise and specialized personnel, which can be time-consuming. Additionally, collecting and processing diatom samples is labor-intensive, requiring specialized equipment and preservation methods. The Diatom Index’s focus on specific stressors may miss other ecological aspects, and potential delays in diatom response could hinder the detection of recent pollution events.
As illustrated in Table 1, despite these challenges, the Diatom Index continues to be an essential tool for assessing the health of river ecosystems. Its advantages include diatoms’ high sensitivity to changes in water quality, making them reliable early indicators of shifts in ecosystem health. Diatom records also offer insights into historical water quality trends, illustrating the effects of human activity and environmental changes over time, as demonstrated in numerous studies [29,30,31]. However, challenges such as the necessity for region-specific indices due to regional variation and the requirement for taxonomic expertise to avoid misidentification and inaccurate assessments persist. The laborious process of collecting and analyzing diatom samples raises concerns about the practicality of the index and the potential for delays in diatom response, thus affecting the timeliness of ecosystem health assessments [32,33,34].
In sum, while microscopy-enabled diatom-based assessments have significantly advanced our understanding and management of aquatic ecosystem health, they are not without limitations. There is a growing need for complementary molecular biological research, such as eDNA analysis, to overcome these limitations and provide a more comprehensive evaluation of water quality and ecosystem health.

3. Advancements in Diatom Research through Molecular Techniques

Molecular techniques have greatly advanced diatom research, revolutionizing the application of established indices and aligning with environmental standards to enhance the evaluation of aquatic ecosystem health. The development of region-specific, multi-metric indices has improved the precision and reliability of data by accounting for regional environmental factors that affect diatom communities (Table 2).
Despite the ongoing reliance on fossil diatoms due to challenges in DNA extraction, microscopic analysis remains the primary method. The difficulty of culturing diatom species, especially in areas where they are scarce, highlights the necessity for accurate morphological species identification to prevent inaccuracies in DNA sequence databases like GenBank.
Molecular classification techniques, developed to complement morphological identification, address the scarcity of expert taxonomists. However, the vital role of taxonomists with morphological expertise in supporting effective molecular classification underscores the importance of both methods. The adoption of environmental DNA (eDNA) analysis for water quality assessment marks a significant advancement, offering a more accurate and comprehensive alternative to traditional visual identification techniques [32,35,36,37]. This method overcomes challenges related to direct counting and subjective interpretation, with advancements in eDNA analysis methods for diatoms emphasizing the need for streamlined field surveys and vigilance against contamination risks during DNA extraction. Establishing robust, standardized protocols is crucial to minimizing these risks [38,39,40].
Recent progress in eDNA analysis has significantly enhanced water quality assessment capabilities in diatom research, highlighting the crucial role of molecular technologies in revealing the ecological importance of diatoms. DNA barcode analysis has become a powerful tool for species identification, overcoming the limitations of morphology-based classifications. Molecular technologies have broadened our understanding of diatom diversity, ecosystem health, and pollution impacts [41,42,43,44], providing deeper insights into diatoms’ roles in nutrient cycling and aquatic nutrient dynamics [45,46,47,48,49]. Furthermore, eDNA sequencing has offered valuable data on diatoms’ historical distribution in coastal regions, providing perspectives on ecosystem changes [50,51,52].
These technological innovations have not only improved water quality assessments but also deepened our understanding of diatom diversity and ecological dynamics. The focus on molecular analysis, particularly eDNA analysis, in diatom research highlights the accuracy and comprehensiveness of modern methods over traditional identification practices. Moreover, it underscores the ongoing challenges and the critical need for precise species identification, the adoption of DNA barcoding, and the extensive application of molecular technologies in elucidating the ecological significance of diatoms. The limitations of molecular techniques underscore the necessity for morphological studies like microscopy analysis to complement and enhance our understanding of diatom research.

4. Advancing River Water Quality Assessment through Integrated Microscopy and Molecular Techniques via Diatom Indices

The shift toward integrating diatom-based indices with environmental DNA (eDNA) analysis marks a pivotal change in the methods used for assessing river ecosystem health. This synthesis explores a holistic approach that merges traditional diatom microscopy with the latest molecular biology advancements. Moving from reliance on diatom indices to the inclusion of molecular techniques has significantly enhanced our understanding and assessment capabilities of river water quality.
Table 3 outlines the strengths and challenges of both traditional diatom analysis and eDNA analysis methods. Our research aims to refine our understanding of these methods’ unique attributes and how they complement one another for a more precise ecosystem evaluation.
Historically, diatom indices like Descy’s Index and the Biological Diatom Index (BDI) have been foundational in quantitative assessments of water quality. These indices utilize the ecological preferences and pollution tolerance of diatoms for nuanced evaluations of nutrient levels, trophic states, and overall ecological health, thus improving the monitoring of aquatic ecosystems. Nonetheless, traditional diatom-based assessments face challenges such as the need for extensive taxonomic expertise and the labor-intensive nature of sample collection and processing. Additionally, the variability in diatom species composition across regions necessitates the development of region-specific indices, highlighting the limitations of traditional methods in capturing the full spectrum of diatom diversity and its implications for water quality.
The adoption of eDNA for water quality assessment represents a major leap in diatom research, courtesy of molecular techniques. Molecular classification and DNA barcode analysis now facilitate species identification, addressing the limitations of morphological classification and the shortage of taxonomic experts [53,54]. These molecular methods enable a more detailed and accurate analysis of diatom communities, shedding light on their ecological roles, diversity, and pollution impacts. The field has been revolutionized by recent advances in eDNA analysis and Next-Generation Sequencing (NGS) technologies, allowing for the precise detection and characterization of diatom communities [55,56,57].
Combining these advancements with traditional microscopy offers a comprehensive view of aquatic ecosystem health, enabling highly accurate and efficient water quality assessments. This integrated approach not only utilizes the strengths of both traditional and modern methodologies but also highlights the importance of advanced bioinformatics tools, standardized quality control protocols, and collaborative research. The development of reference databases and the application of metabarcoding techniques are crucial for ensuring the reliability and comprehensiveness of diatom-based assessments [58,59,60,61,62]. Moreover, cross-validating eDNA results with traditional methods emphasizes the value of a multifaceted approach to river water quality evaluation [41,63,64].
Therefore, the integration of microscopic examination with molecular analysis in diatom research signifies a major advancement in our ability to assess and monitor river ecosystem health. By leveraging the benefits of both traditional and modern methods, researchers and environmental managers can achieve a more nuanced and effective water quality assessment methods [25,62,65]. This integrated approach underscores the ongoing need for advanced bioinformatics expertise, collaborative research, and continuous innovation in the pursuit of sustainable water management and conservation strategies.

5. Advancing River Health Assessments: The Future of Diatom-Based Strategies

This comprehensive review sheds light on the development and underlying principles of diatom-based water quality assessments, identifying the lack of universal standards as a critical challenge. This issue stems from difficulties in standardizing methods and the limited taxonomic resolution in environmental DNA (eDNA) analyses. It underscores the urgent need for comprehensive reference libraries that reflect the diversity and ecological intricacies of diatom species across various regions [66,67]. It also emphasizes the importance of enhancing DNA barcoding and metabarcoding techniques to identify diatom-specific unique barcode regions, thereby improving species identification precision and ecological assessments [68].
This review highlights the transformative potential of Next-Generation Sequencing (NGS) technologies, including metagenomics and metatranscriptomics, in advancing diatom-based evaluations. Automating the sampling and analysis process is crucial for reducing subjective biases and streamlining research. The necessity for interdisciplinary collaboration is accentuated, combining expertise from ecology, molecular biology, bioinformatics, and environmental science to address the complex challenges of aquatic ecosystem assessment [69,70]. The significance of long-term studies in capturing the spatial and temporal dynamics of ecosystems and their role in documenting the effects of seasonal changes, climate variations, and human activities on water bodies is also highlighted.
Despite molecular advancements, the importance of traditional morphological taxonomy expertise is acknowledged, with a call for focused training and educational efforts to maintain diatom identification accuracy. The reliance on conventional methods and the subjective nature of microscopic analysis highlight the need for more objective and reproducible assessment tools. Furthermore, there is an advocacy for developing automated imaging and analysis systems to minimize human error [71,72,73].
This paper acknowledges the challenges related to contamination and the complexity of processing environmental samples in eDNA studies. It calls for research into more effective sample processing techniques and contamination control strategies. Moreover, the high costs and time requirements of comprehensive diatom and eDNA analyses are recognized as barriers, necessitating the development of more cost-effective and efficient methods and technologies [62,74,75].
While recognizing advances in understanding diatom biology and ecology, this article points out remaining gaps in knowledge about diatom responses to environmental stressors and their roles in nutrient cycling. It advocates for expanded research efforts to address these gaps, aiming to enhance our understanding of aquatic ecosystems and refine assessment methodologies [1,76,77].
Collectively, the significant progress made in assessing river ecosystem health and emphasizes the ongoing need for research. By addressing the identified challenges and employing innovative strategies, collaborative efforts, and cutting-edge technologies, the precision, accuracy, and relevance of diatom-based water quality assessments can be greatly enhanced, ensuring their effectiveness amidst environmental changes.

6. Conclusions

This review underscores the significant advancements in river ecosystem health assessment achieved through the integration of traditional diatom-based methodologies and modern molecular techniques, particularly environmental DNA (eDNA) analysis. By leveraging the strengths of both approaches, this integrated strategy enhances the precision, accuracy, and comprehensiveness of aquatic ecosystem evaluations. This study emphasizes the importance of developing comprehensive reference libraries, advanced bioinformatics tools, and standardized protocols to address current challenges in species identification and assessment standardization. Furthermore, it calls for continued innovation, interdisciplinary collaboration, and research to refine and enhance diatom-based water quality assessments. This approach not only improves our understanding of aquatic ecosystems but also ensures the effectiveness of monitoring and management strategies in response to ongoing environmental transformations, highlighting the critical role of diatoms as indicators of ecosystem health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16060853/s1, Supplement S1: Advancements in diatom indices for evaluating aquatic ecological health and water quality. The table references data from studies conducted by Kim et al. [28] and Masouras et al. [29] for all mentioned indices.

Author Contributions

H.-K.K., I.-H.C., E.-A.H., B.-H.H. and B.-H.K. conceptualized the research; H.-K.K., I.-H.C., E.-A.H., B.-H.H. and B.-H.K. conducted the fieldwork; H.-K.K., I.-H.C., E.-A.H., B.-H.H. and B.-H.K. analyzed the data; H.-K.K. and B.-H.K. wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study did not receive external funding.

Acknowledgments

We extend our gratitude to the anonymous reviewers for their valuable and constructive comments.

Conflicts of Interest

Author In-Hwan Cho was employed by the company Migang E&C Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Cantonati, M.; Poikane, S.; Pringle, C.M.; Stevens, L.E.; Turak, E.; Heino, J.; Richardson, J.S.; Bolpagni, R.; Borrini, A.; Cid, N.; et al. Characteristics, Main Impacts, and Stewardship of Natural and Artificial Freshwater Environments: Consequences for Biodiversity Conservation. Water 2020, 12, 260. [Google Scholar] [CrossRef]
  2. Ding, R.; Yu, K.; Fan, Z.; Liu, J. Study and Application of Urban Aquatic Ecosystem Health Evaluation Index System in River Network Plain Area. Int. J. Environ. Res. Public Health 2022, 19, 16545. [Google Scholar] [CrossRef]
  3. Fu, L.; Dong, X.; Shen, H.; Zhu, T.; Sun, F. River Ecosystem Health Assessment in Rapid Urbanization Regions (Shenzhen, China) under the Guidance of Bioremediation Objectives. Water 2023, 15, 3859. [Google Scholar] [CrossRef]
  4. Hu, X.; Zuo, D.; Xu, Z.; Huang, Z.; Liu, B.; Han, Y.; Bi, Y. Response of macroinvertebrate community to water quality factors and aquatic ecosystem health assessment in a typical river in Beijing, China. Environ. Res. 2022, 212, 113474. [Google Scholar] [CrossRef] [PubMed]
  5. Ibisch, P.L.; Hobson, P.; Vega, A. Mutual mainstreaming of biodiversity conservation and human development: Towards a more radical Ecosystem Approach. In Interdependence of Biodiversity and Development under Global Change; Ibisch, P.L., Vega, E., Herrmann, T.M., Eds.; Technical Series No. 54; Secretariat of the Convention on Biological Diversity: Montreal, QC, Canada, 2010; pp. 15–34. Available online: http://www.cbd.int/doc/publications/cbd-ts-54-en.pdf (accessed on 11 February 2024).
  6. Li, M.; Li, L.; Strielkowski, W. The Impact of Urbanization and Industrialization on Energy Security: A Case Study of China. Energies 2019, 12, 2194. [Google Scholar] [CrossRef]
  7. Liu, L.; Xu, Z.; Yin, X.; Li, F.; Dou, T. Development of a multimetric index based on benthic macroinvertebrates for the assessment of urban stream health in Jinan City, China. Environ. Monit. Assess. 2017, 189, 205. [Google Scholar] [CrossRef] [PubMed]
  8. Ebrahimzadeh, G.; Alimohammadi, M.; Kahkah, M.R.R.; Mahvi, A.H. Relationship between algae diversity and water quality—A case study: Chah Niemeh reservoir Southeast of Iran. J. Environ. Health Sci. Eng. 2021, 19, 437–443. [Google Scholar] [CrossRef]
  9. Markad, A.T.; Landge, A.T.; Nayak, B.B.; Inamdar, A.B.; Mishra, A.K. Trophic state modeling for shallow freshwater reservoir: A new approach. Environ. Monit. Assess. 2019, 191, 586. [Google Scholar] [CrossRef]
  10. Yang, Y.; Cao, J.-X.; Pei, G.-F.; Liu, G.-X. Using benthic diatom assemblages to assess human impacts on streams across a rural to urban gradient. Environ. Sci. Pollut. Res. 2015, 22, 18093–18106. [Google Scholar] [CrossRef]
  11. Ceschin, S.; Bellini, A.; Scalici, M. Aquatic plants and ecotoxicological assessment in freshwater ecosystems: A review. Environ. Sci. Pollut. Res. 2021, 28, 4975–4988. [Google Scholar] [CrossRef]
  12. Rakib, R.J.; Jolly, Y.N.; Begum, B.A.; Choudhury, T.R.; Fatema, K.J.; Islam, S.; Ali, M.M.; Idris, A.M. Assessment of trace element toxicity in surface water of a fish breeding river in Bangladesh: A novel approach for ecological and health risk evaluation. Toxin Rev. 2022, 41, 420–436. [Google Scholar] [CrossRef]
  13. Schwarzenbach, R.P.; Egli, T.; Hofstetter, T.B.; Von Gunten, U.V.; Wehrli, B. Global Water Pollution and Human Health. Annu. Rev. Environ. Resour. 2010, 35, 109–136. [Google Scholar] [CrossRef]
  14. Choi, I.-C.; Shin, H.-J.; Nguyen, T.T.; Tenhunen, J. Water Policy Reforms in South Korea: A Historical Review and Ongoing Challenges for Sustainable Water Governance and Management. Water 2017, 9, 717. [Google Scholar] [CrossRef]
  15. Kwak, J.I.; Nam, S.-H.; An, Y.-J. Water quality standards for the protection of human health and aquatic ecosystems in Korea: Current state and future perspective. Environ. Sci. Pollut. Res. 2018, 25, 3108–3119. [Google Scholar] [CrossRef] [PubMed]
  16. Feio, M.J.; Hughes, R.M.; Callisto, M.; Nichols, S.J.; Odume, O.N.; Quintella, B.R.; Kuemmerlen, M.; Aguiar, F.C.; Almeida, S.F.; Alonso-Eguíalis, P.; et al. The Biological Assessment and Rehabilitation of the World’s Rivers: An Overview. Water 2021, 13, 371. [Google Scholar] [CrossRef] [PubMed]
  17. Li, Z.; Ma, C.; Sun, Y.; Lu, X.; Fan, Y. Ecological health evaluation of rivers based on phytoplankton biological integrity index and water quality index on the impact of anthropogenic pollution: A case of Ashi River Basin. Front. Microbiol. 2022, 13, 942205. [Google Scholar] [CrossRef] [PubMed]
  18. Woodward, G.; Gessner, M.O.; Giller, P.S.; Gulis, V.; Hladyz, S.; Lecerf, A.; Malmqvist, B.; McKie, B.G.; Tiegs, S.D.; Cariss, H.; et al. Continental-Scale Effects of Nutrient Pollution on Stream Ecosystem Functioning. Science 2012, 336, 1438–1440. [Google Scholar] [CrossRef] [PubMed]
  19. Ahmad, A.; Imran, M.; Ahsan, H. Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics 2023, 15, 1630. [Google Scholar] [CrossRef]
  20. Brantschen, J.; Blackman, R.C.; Walser, J.-C.; Altermatt, F. Environmental DNA gives comparable results to morphology-based indices of macroinvertebrates in a large-scale ecological assessment. PLoS ONE 2021, 16, e0257510. [Google Scholar] [CrossRef]
  21. Hellal, J.; Barthelmebs, L.; Bérard, A.; Cébron, A.; Cheloni, G.; Colas, S.; Cravo-Laureau, C.; De Clerck, C.; Gallois, N.; Hery, M.; et al. Unlocking secrets of microbial ecotoxicology: Recent achievements and future challenges. FEMS Microbiol. Ecol. 2023, 99, fiad102. [Google Scholar] [CrossRef]
  22. Tan, X.; Zhang, Q.; Burford, M.A.; Sheldon, F.; Bunn, S.E. Benthic Diatom Based Indices for Water Quality Assessment in Two Subtropical Streams. Front. Microbiol. 2017, 8, 601. [Google Scholar] [CrossRef] [PubMed]
  23. Nhiwatiwa, T.; Dalu, T.; Sithole, T. Assessment of river quality in a subtropical Austral river system: A combined approach using benthic diatoms and macroinvertebrates. Appl. Water Sci. 2017, 7, 4785–4792. [Google Scholar] [CrossRef]
  24. Leese, F.; Bouchez, A.; Abarenkov, K.; Altermatt, F.; Borja, A.; Bruce, K.; Ekrem, T.; Ciampor, F., Jr.; Ciamporova-Zatovicova, Z.; Costa, F.O.; et al. Chapter Two—Why We Need Sustainable Networks Bridging Countries, Disciplines, Cultures and Generations for Aquatic Biomonitoring 2.0: A Perspective Derived from the DNAqua-Net COST Action. In Advances in Ecological Research; Bohan, D.A., Dumbrell, A.J., Woodward, G., Jackson, M., Eds.; Academic Press: Cambridge, MA, USA, 2018; Volume 58, pp. 63–99. [Google Scholar] [CrossRef]
  25. Jo, T.S. Utilizing the state of environmental DNA (eDNA) to incorporate time-scale information into eDNA analysis. Proc. R. Soc. B Biol. Sci. 2023, 290, 20230979. [Google Scholar] [CrossRef] [PubMed]
  26. Balasingham, K.D.; Walter, R.P.; Heath, D.D. Residual eDNA detection sensitivity assessed by quantitative real-time PCR in a river ecosystem. Mol. Ecol. Resour. 2017, 17, 523–532. [Google Scholar] [CrossRef] [PubMed]
  27. Pan, G.; Xu, Y.; Yu, Z.; Song, S.; Zhang, Y. Analysis of river health variation under the background of urbanization based on entropy weight and matter-element model: A case study in Huzhou City in the Yangtze River Delta, China. Environ. Res. 2015, 139, 31–35. [Google Scholar] [CrossRef] [PubMed]
  28. Kim, H.-K.; Cho, I.-H.; Hwang, E.-A.; Kim, Y.-J.; Kim, B.-H. Benthic Diatom Communities in Korean Estuaries: Species Appearances in Relation to Environmental Variables. Int. J. Environ. Res. Public Health 2019, 16, 2681. [Google Scholar] [CrossRef] [PubMed]
  29. Masouras, A.; Karaouzas, I.; Dimitriou, E.; Tsirtsis, G.; Smeti, E. Benthic Diatoms in River Biomonitoring—Present and Future Perspectives within the Water Framework Directive. Water 2021, 13, 478. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Peng, W.; Li, Y.; Xiao, X.; Hillman, A. Diatom-based inferences of environmental changes from an alpine lake on the southeast edge of the Tibetan plateau over the last 4000 years. Front. Earth Sci. 2024, 12, 1324724. [Google Scholar] [CrossRef]
  31. Hasan, M.; Gani, A.; Alfasane, A.; Ayesha, M.; Nahar, K. Benthic diatom communities and a comparative seasonal-based ecological quality assessment of a transboundary river in Bangladesh. PLoS ONE 2023, 18, e0291751. [Google Scholar] [CrossRef]
  32. Bueno, G.; Deniz, O.; Pedraza, A.; Ruiz-Santaquiteria, J.; Salido, J.; Cristóbal, G.; Borrego-Ramos, M.; Blanco, S. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Appl. Sci. 2017, 7, 753. [Google Scholar] [CrossRef]
  33. Kutty, S.N.; Loh, R.K.; Bannister, W.; Taylor, D. Evaluation of a diatom eDNA-based technique for assessing water quality variations in tropical lakes and reservoirs. Ecol. Indic. 2022, 141, 109108. [Google Scholar] [CrossRef]
  34. Pinto, R.; Vilarinho, R.; Carvalho, A.P.; Moreira, J.A.; Guimarães, L.; Oliva-Teles, L. Raman spectroscopy applied to diatoms (microalgae, Bacillariophyta): Prospective use in the environmental diagnosis of freshwater ecosystems. Water Res. 2021, 198, 117102. [Google Scholar] [CrossRef] [PubMed]
  35. Burfeid-Castellanos, A.M.; Kloster, M.; Beszteri, S.; Postel, U.; Spyra, M.; Zurowietz, M.; Nattkemper, T.W.; Beszteri, B. A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples. Water 2022, 14, 3332. [Google Scholar] [CrossRef]
  36. Andersson, B.; Godhe, A.; Filipsson, H.L.; Rengefors, K.; Berglund, O. Differences in metal tolerance among strains, populations, and species of marine diatoms—Importance of exponential growth for quantification. Aquat. Toxicol. 2020, 226, 105551. [Google Scholar] [CrossRef] [PubMed]
  37. Burfeid-Castellanos, A.M.; Martín-Martín, R.P.; Kloster, M.; Angulo-Preckler, C.; Avila, C.; Beszteri, B. Epiphytic diatom community structure and richness is determined by macroalgal host and location in the South Shetland Islands (Antarctica). PLoS ONE 2021, 16, e0250629. [Google Scholar] [CrossRef]
  38. Ficetola, G.F.; Miaud, C.; Pompanon, F.; Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 2008, 4, 423–425. [Google Scholar] [CrossRef] [PubMed]
  39. Dejean, T.; Valentini, A.; Duparc, A.; Pellier-Cuit, S.; Pompanon, F.; Taberlet, P.; Miaud, C. Persistence of Environmental DNA in Freshwater Ecosystems. PLoS ONE 2011, 6, e23398. [Google Scholar] [CrossRef] [PubMed]
  40. Valentini, A.; Taberlet, P.; Miaud, C.; Civade, R.; Herder, J.; Thomsen, P.F.; Bellemain, E.; Besnard, A.; Coissac, E.; Boyer, F.; et al. Next-generation Monitoring of Aquatic Biodiversity Using Environmental DNA Metabarcoding. Mol. Ecol. 2016, 25, 929–942. [Google Scholar] [CrossRef]
  41. Fediajevaite, J.; Priestley, V.; Arnold, R.; Savolainen, V. Meta-analysis shows that environmental DNA outperforms traditional surveys, but warrants better reporting standards. Ecol. Evol. 2021, 11, 4803–4815. [Google Scholar] [CrossRef]
  42. Papaioannou, C.; Geladakis, G.; Kommata, V.; Batargias, C.; Lagoumintzis, G. Insights in Pharmaceutical Pollution: The Pro-spective Role of eDNA Metabarcoding. Toxics 2023, 11, 903. [Google Scholar] [CrossRef]
  43. Wang, Y.; Liu, S.; Wang, J.; Yao, Y.; Chen, Y.; Xu, Q.; Zhao, Z.; Chen, N. Diatom Biodiversity and Speciation Revealed by Comparative Analysis of Mitochondrial Genomes. Front. Plant Sci. 2022, 13, 749982. [Google Scholar] [CrossRef] [PubMed]
  44. Zanovello, L.; Girardi, M.; Marchesini, A.; Galla, G.; Casari, S.; Micheletti, D.; Endrizzi, S.; Fedrigotti, C.; Pedrini, P.; Bertorelle, G.; et al. A validated protocol for eDNA-based monitoring of within-species genetic diversity in a pond-breeding amphibian. Sci. Rep. 2023, 13, 4346. [Google Scholar] [CrossRef] [PubMed]
  45. Gregersen, R.; Pearman, J.K.; Atalah, J.; Waters, S.; Vandergoes, M.J.; Howarth, J.D.; Thomson-Laing, G.; Thompson, L.; Wood, S.A. A taxonomy-free diatom eDNA-based technique for assessing lake trophic level using lake sediments. J. Environ. Manag. 2023, 345, 118885. [Google Scholar] [CrossRef] [PubMed]
  46. Lin, Y.; Zhong, W.; Zhang, X.; Zhou, X.; He, L.; Lv, J.; Zhao, Z. Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers. Ecol. Evol. 2023, 13, e10088. [Google Scholar] [CrossRef] [PubMed]
  47. Machuca-Sepúlveda, J.; Miranda, J.; Lefin, N.; Pedroso, A.; Beltrán, J.F.; Farias, J.G. Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring. Biology 2023, 12, 923. [Google Scholar] [CrossRef] [PubMed]
  48. Jain, R.; Gaur, A.; Suravajhala, R.; Chauhan, U.; Pant, M.; Tripathi, V.; Pant, G. Microplastic pollution: Understanding microbial degradation and strategies for pollutant reduction. Sci. Total Environ. 2023, 905, 167098. [Google Scholar] [CrossRef] [PubMed]
  49. Xue, H.; Zheng, B.; Meng, F.; Wang, Y.; Zhang, L.; Cheng, P. Assessment of Aquatic Ecosystem Health of the Wutong River Based on Benthic Diatoms. Water 2019, 11, 727. [Google Scholar] [CrossRef]
  50. Armbrecht, L.; Weber, M.E.; Raymo, M.E.; Peck, V.L.; Williams, T.; Warnock, J.; Kato, Y.; Hernández-Almeida, I.; Hoem, F.; Reilly, B.; et al. Ancient Marine Sediment DNA Reveals Diatom Transition in Antarctica. Nat. Commun. 2022, 13, 5787. [Google Scholar] [CrossRef]
  51. Clark, D.; Pilditch, C.; Pearman, J.; Ellis, J.; Zaiko, A. Environmental DNA metabarcoding reveals estuarine benthic community response to nutrient enrichment—Evidence from an in-situ experiment. Environ. Pollut. 2020, 267, 115472. [Google Scholar] [CrossRef]
  52. Djurhuus, A.; Closek, C.J.; Kelly, R.P.; Pitz, K.J.; Michisaki, R.P.; Starks, H.A.; Walz, K.R.; Andruszkiewicz, E.A.; Olesin, E.; Hubbard, K.; et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 2020, 11, 254. [Google Scholar] [CrossRef]
  53. Rivera, A.J.; Pérez-Godoy, M.D.; Elizondo, D.; Deka, L.; del Jesus, M.J. Analysis of clustering methods for crop type mapping using satellite imagery. Neurocomputing 2022, 492, 91–106. [Google Scholar] [CrossRef]
  54. Van Rossum, T.; Ferretti, P.; Maistrenko, O.M.; Bork, P. Diversity within species: Interpreting strains in microbiomes. Nat. Rev. Microbiol. 2020, 18, 491–506. [Google Scholar] [CrossRef] [PubMed]
  55. Mittal, D.; Mease, R.; Kuner, T.; Flor, H.; Kuner, R.; Andoh, J. Data management strategy for a collaborative research center. GigaScience 2022, 12, giad049. [Google Scholar] [CrossRef] [PubMed]
  56. Penjor, U.; Kaszta, Ż.; Macdonald, D.W.; Cushman, S.A. Prioritizing areas for conservation outside the existing protected area network in Bhutan: The use of multi-species, multi-scale habitat suitability models. Landsc. Ecol. 2021, 36, 1281–1309. [Google Scholar] [CrossRef]
  57. Trakhtenbrot, A.; Kadmon, R. Effectiveness of Environmental Cluster Analysis in Representing Regional Species Diversity. Conserv. Biol. 2006, 20, 1087–1098. [Google Scholar] [CrossRef] [PubMed]
  58. Bailet, B.; Bouchez, A.; Franc, A.; Frigerio, J.-M.; Keck, F.; Karjalainen, S.-M.; Rimet, F.; Schneider, S.; Kahlert, M. Molecular versus morphological data for benthic diatoms biomonitoring in Northern Europe freshwater and consequences for ecological status. Metabarcoding Metagenomics 2019, 3, e34002. [Google Scholar] [CrossRef]
  59. Smucker, N.J.; Pilgrim, E.M.; Nietch, C.T.; Darling, J.A.; Johnson, B.R. DNA metabarcoding effectively quantifies diatom responses to nutrients in streams. Ecol. Appl. 2020, 30, e02205. [Google Scholar] [CrossRef]
  60. Luddington, I.A.; Kaczmarska, I.; Lovejoy, C. Distance and Character-Based Evaluation of the V4 Region of the 18S rRNA Gene for the Identification of Diatoms (Bacillariophyceae). PLoS ONE 2012, 7, e45664. [Google Scholar] [CrossRef]
  61. Jacobs-Palmer, E.; Gallego, R.; Cribari, K.; Keller, A.G.; Kelly, R.P. Environmental DNA Metabarcoding for Simultaneous Monitoring and Ecological Assessment of Many Harmful Algae. Front. Ecol. Evol. 2021, 9, 612107. [Google Scholar] [CrossRef]
  62. Ruppert, K.M.; Kline, R.J.; Rahman, S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 2019, 17, e00547. [Google Scholar] [CrossRef]
  63. Thalinger, B.; Deiner, K.; Harper, L.R.; Rees, H.C.; Blackman, R.C.; Sint, D.; Traugott, M.; Goldberg, C.S.; Bruce, K. A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environ. DNA 2021, 3, 823–836. [Google Scholar] [CrossRef]
  64. Nagarajan, R.P.; Bedwell, M.; Holmes, A.E.; Sanches, T.; Acuña, S.; Baerwald, M.; Barnes, M.A.; Blankenship, S.; Connon, R.E.; Deiner, K.; et al. Environmental DNA Methods for Ecological Monitoring and Biodiversity Assessment in Estuaries. Estuaries Coasts 2022, 45, 2254–2273. [Google Scholar] [CrossRef]
  65. Banerjee, P.; Dey, G.; Antognazza, C.M.; Sharma, R.K.; Maity, J.P.; Chan, M.W.Y.; Huang, Y.-H.; Lin, P.-Y.; Chao, H.-C.; Lu, C.-M.; et al. Reinforcement of Environmental DNA Based Methods (Sensu Stricto) in Biodiversity Monitoring and Conservation: A Review. Biology 2021, 10, 1223. [Google Scholar] [CrossRef] [PubMed]
  66. Rimet, F.; Gusev, E.; Kahlert, M.; Kelly, M.G.; Kulikovskiy, M.; Maltsev, Y.; Mann, D.G.; Pfannkuchen, M.; Trobajo, R.; Vasselon, V.; et al. Diat.barcode, an open-access curated barcode library for diatoms. Sci. Rep. 2019, 9, 15116. [Google Scholar] [CrossRef] [PubMed]
  67. Zimmermann, J.; Abarca, N.; Enke, N.; Skibbe, O.; Kusber, W.-H.; Jahn, R. Taxonomic reference libraries for environmental barcoding: A best practice example from diatom research. PLoS ONE 2014, 9, e108793. [Google Scholar] [CrossRef] [PubMed]
  68. Gostel, M.R.; Kress, W.J. The Expanding Role of DNA Barcodes: Indispensable Tools for Ecology, Evolution, and Conservation. Diversity 2022, 14, 213. [Google Scholar] [CrossRef]
  69. Satam, H.; Joshi, K.; Mangrolia, U.; Waghoo, S.; Zaidi, G.; Rawool, S.; Thakare, R.P.; Banday, S.; Mishra, A.K.; Das, G.; et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 2023, 12, 997. [Google Scholar] [CrossRef] [PubMed]
  70. Oulas, A.; Pavloudi, C.; Polymenakou, P.; Pavlopoulos, G.A.; Papanikolaou, N.; Kotoulas, G.; Arvanitidis, C.; Iliopoulos, L. Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies. Bioinform. Biol. Insights 2015, 9, 75–88. [Google Scholar] [CrossRef]
  71. Yang, B.; Zhang, Z.; Yang, C.-Q.; Wang, Y.; Orr, M.C.; Wang, H.; Zhang, A.-B. Identification of Species by Combining Molecular and Morphological Data Using Convolutional Neural Networks. Syst. Biol. 2022, 71, 690–705. [Google Scholar] [CrossRef]
  72. Manoylov, K.M. Taxonomic identification of algae (morphological and molecular): Species concepts, methodologies, and their implications for ecological bioassessment. J. Phycol. 2014, 50, 409–424. [Google Scholar] [CrossRef]
  73. Kloster, M.; Langenkämper, D.; Zurowietz, M.; Beszteri, B.; Nattkemper, T.W. Deep learning-based diatom taxonomy on virtual slides. Sci. Rep. 2020, 10, 14416. [Google Scholar] [CrossRef]
  74. Wee, A.K.S.; Salmo, S., III; Sivakumar, K.; Then, A.Y.-H.; Basyuni, M.; Fall, J.; Habib, K.A.; Isowa, Y.; Leopardas, V.; Peer, N.; et al. Prospects and challenges of environmental DNA (eDNA) metabarcoding in mangrove restoration in Southeast Asia. Front. Mar. Sci. 2023, 10, 1033258. [Google Scholar] [CrossRef]
  75. Fonseca, V.G.; Davison, P.I.; Creach, V.; Stone, D.; Bass, D.; Tidbury, H.J. The Application of eDNA for Monitoring Aquatic Non-Indigenous Species: Practical and Policy Considerations. Diversity 2023, 15, 631. [Google Scholar] [CrossRef]
  76. Chen, J.; Huang, Y.; Shu, Y.; Hu, X.; Wu, D.; Jiang, H.; Wang, K.; Liu, W.; Fu, W. Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity. Front. Bioeng. Biotechnol. 2022, 10, 908804. [Google Scholar] [CrossRef]
  77. Wu, N.; Dong, X.; Liu, Y.; Wang, C.; Baattrup-Pedersen, A.; Riis, T. Using river microalgae as indicators for freshwater biomonitoring: Review of published research and future directions. Ecol. Indic. 2017, 81, 124–131. [Google Scholar] [CrossRef]
Table 1. Simplified advantages and challenges of using the diatom index for river health assessment.
Table 1. Simplified advantages and challenges of using the diatom index for river health assessment.
Diatom IndexDescription
AdvantagesSensitivityDiatoms quickly indicate changes in water quality, such as pollution and nutrient levels, signaling ecosystem health shifts.
Integrative indicatorThey reflect cumulative environmental stressors, offering a comprehensive view of river health and long-term trends.
Historical insightsDiatoms help track historical water quality changes, showing the effects of human and environmental shifts.
Non-destructive samplingThey are collected harmlessly, preserving river ecosystems while assessing health.
Early warningDiatoms alert to water quality issues early, preventing severe problems.
ChallengesRegional variationDiatom sensitivity varies regionally, necessitating tailored indices for accurate health assessments.
Taxonomic expertiseIdentifying diatoms accurately demands specialized knowledge, posing challenges for assessment accuracy.
Sample handlingCollecting and processing diatom samples requires effort and specific equipment, with careful handling needed for sample integrity.
Limited scopeThe diatom index mainly detects changes related to diatoms, possibly missing broader ecosystem health indicators.
Lag timeEnvironmental changes may not immediately reflect in diatom communities, potentially delaying pollution detection.
Table 2. Simplified advancing methods and tools for diatom research.
Table 2. Simplified advancing methods and tools for diatom research.
TechniquesMethods (Tools)
MicroscopySimplified to essential imaging techniques and software for diatom study, focusing on confocal and electron microscopy for morphology analysis (confocal microscopy, electron microscopy, basic image analysis).
Molecular MethodsStreamlined genomic analysis utilizing Next-Generation Sequencing and CRISPR-Cas9 for targeted gene editing, with a focus on essential DNA and RNA analysis (NGS, CRISPR-Cas9, DNA/RNA analysis).
Culture and AutomationReduced to core automated culture and sample processing techniques for efficient diatom research (automated diatom culture, sample processing, DNA extraction, PCR).
Collaborative ResearchFocused on key interdisciplinary collaborations using computational models and data sharing for diatom biology and ecology (interdisciplinary projects, data sharing).
Bioinformatics and Data AnalysisCondensed to fundamental bioinformatics and data analysis techniques, with an emphasis on sequence analysis and taxonomic categorization using machine learning (bioinformatics and machine learning for data analysis).
Table 3. Simplified strengths and challenges of diatom and eDNA analyses.
Table 3. Simplified strengths and challenges of diatom and eDNA analyses.
AspectDiatom AnalysiseDNA Analysis
StrengthsProvides comprehensive insights from specific to broad ecosystem views with enhanced sensitivity
Offers efficient, precise evaluation by combining both methods		Potential for diverse species detection using DNA markers
Can detect a wider range of organisms beyond diatoms
Non-invasive, reducing ecological disruption
Suitable for long-term monitoring
Provides a more comprehensive view of ecosystem health
Utilizes DNA sequencing with the potential for automated identification
Offers potential for more standardized protocols
ChallengesDependent on taxonomic skills
Requires investment in microscopes and personnel
Demands taxonomic expertise\
Labor-intensive with potential identification errors
Involves physical sample collection with potential ecological disruption
Requires consistent access to skilled personnel
Shows variable sample representativeness
Subject to variability and taxonomic revisions		Requires lab setup and molecular biology equipment costs
Requires bioinformatics skills for DNA sequence analysis
Faces technical challenges in DNA analysis, an evolving field with sequencing challenges
Detects species presence, potentially with less contextual information
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kim, H.-K.; Cho, I.-H.; Hwang, E.-A.; Han, B.-H.; Kim, B.-H. Advancing River Health Assessments: Integrating Microscopy and Molecular Techniques through Diatom Indices. Water 2024, 16, 853. https://doi.org/10.3390/w16060853

AMA Style

Kim H-K, Cho I-H, Hwang E-A, Han B-H, Kim B-H. Advancing River Health Assessments: Integrating Microscopy and Molecular Techniques through Diatom Indices. Water. 2024; 16(6):853. https://doi.org/10.3390/w16060853

Chicago/Turabian Style

Kim, Ha-Kyung, In-Hwan Cho, Eun-A Hwang, Byung-Hun Han, and Baik-Ho Kim. 2024. "Advancing River Health Assessments: Integrating Microscopy and Molecular Techniques through Diatom Indices" Water 16, no. 6: 853. https://doi.org/10.3390/w16060853

APA Style

Kim, H. -K., Cho, I. -H., Hwang, E. -A., Han, B. -H., & Kim, B. -H. (2024). Advancing River Health Assessments: Integrating Microscopy and Molecular Techniques through Diatom Indices. Water, 16(6), 853. https://doi.org/10.3390/w16060853

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop