Development of a collaborative web platform documenting the diversity and extent of diatom deformities

References

• Cemagref. 1982. Étude des méthodes biologiques d’appréciation quantitative de la qualité des eaux. Rapport Cemagref QE Lyon-AF Bassin Rhône Méditerranée Corse. 128.
   Google Scholar
• Cerisier, A, J Vedrenne, I Lavoie, S Morin. 2019. Assessing the severity of diatom deformities using geometric morphometry. Bot Lett. 166(1):32–40. doi:10.1080/23818107.2018.1474800.
   Web of Science ®Google Scholar
• Coste, M, S Boutry, J Tison-Rosebery, F Delmas. 2009. Improvements of the Biological Diatom Index (BDI): description and efficiency of the new version (BDI-2006). Ecol Indic. 9(4):621–650. doi:10.1016/j.ecolind.2008.06.003.
   Web of Science ®Google Scholar
• Falasco, E, L Ector, CE Wetzel, G Badino, F Bona. 2021. Looking back, looking forward: a review of the new literature on diatom teratological forms (2010–2020). Hydrobiologia. 848(8):1675–1753. doi:10.1007/s10750-021-04540-x.
   Web of Science ®Google Scholar
• Kloster, M, O Esper, G Kauer, B Beszteri. 2017. Large-Scale Permanent Slide Imaging and Image Analysis for Diatom Morphometrics. Applied Sciences. 7(4):330. doi:10.3390/app7040330.
   Google Scholar
• Kloster, M, G Kauer, B Beszteri. 2014. SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects. BMC Bioinform. 15(1):218. doi:10.1186/1471-2105-15-218.
   PubMedGoogle Scholar
• Lavoie, I, PB Hamilton, S Morin, S Kim Tiam, S Gonçalves, E Falasco, C Fortin, B Gontero, D Heudre, M Kojadinovic-Sirinelli, et al. 2017. Diatom teratologies as biomarkers of contamination: are all deformities ecologically meaningful? Ecol Indic. 82:539–550. doi:10.1016/j.ecolind.2017.06.048.
   Web of Science ®Google Scholar
• Lowe, RL, Y Pan. 1996. Benthic algal communities as biological monitors. In Algal Ecology: freshwater Benthic Ecosystems. Aquatic Ecology Series, edited byStevenson, R.J., Bothwell, M.L., Lowe, R.L., 705–739. San Diego, CA, USA: Academic Press Inc.
   Google Scholar
• Millan, F, C Izere, V Breton, O Voldoire, DG Biron, CE Wetzel, D Miallier, E Allain, L Ector, A Beauger. 2020. The effect of natural radioactivity on diatom communities in mineral springs. Bot Lett. 167(1):95–113. doi:10.1080/23818107.2019.1691051.
   Web of Science ®Google Scholar
• Morin, S, A Cordonier, I Lavoie, A Arini, S Blanco, TT Duong, E Tornés, B Bonet, N Corcoll, L Faggiano, et al. 2012. Consistency in diatom response to metal-contaminated environments. In Emerging and priority pollutants in rivers, edited by Guasch, H., Ginebreda, A., Geiszinger, A., Vol. 19, 117–146. Heidelberg, Germany: Springer.
   Google Scholar
• Morin, S, N Gómez, E Tornes, M Licursi, J Tison-Rosebery (2016). In Benthic diatom monitoring and assessment of freshwater environments: standard methods and future challenges, edited by Romani, AM, Guasch, H, Balaguer, MD, 111–124. Norfolk, UK: Caister Academic Press.
   Google Scholar
• Olenici, A, C Baciu, S Blanco, S Morin. 2020. Naturally and Environmentally Driven Variations in Diatom Morphology: Implications for Diatom-Based Assessment of Water Quality. In Modern Trends in Diatom Identification, edited by Cristóbal, G, Blanco, S, Bueno, G, Vol. 10, 39–50. Switzerland: Springer, Cham.
   Google Scholar
• Olenici, A, S Blanco, M Borrego-Ramos, L Momeu, C Baciu. 2017. Exploring the effects of acid mine drainage on diatom teratology using geometric morphometry. Ecotoxicology. 26(8):1018–1030. doi:10.1007/s10646-017-1830-3.
   PubMed Web of Science ®Google Scholar
• Orenstein, EC, S-D Ayata, F Maps, ÉC Becker, F Benedetti, T Biard, T de Garidel-Thoron, JS Ellen, F Ferrario, SLC Giering, et al. 2022. Machine learning techniques to characterize functional traits of plankton from image data. Limnology Oceanography. 67(8):1647–1669. doi:10.1002/lno.12101.
   PubMed Web of Science ®Google Scholar
• Pandey, LK, EA Bergey, J Lyu, J Park, S Choi, H Lee, S Depuydt, YT Oh, SM Lee, T Han. 2017. The use of diatoms in ecotoxicology and bioassessment: insights, advances and challenges. Water Res. 118:39–58. doi:10.1016/j.watres.2017.01.062.
   PubMed Web of Science ®Google Scholar
• Spaulding, SA, MG Potapova, IW Bishop, SS Lee, TS Gasperak, E Jovanoska, PC Furey, MB Edlund. 2021. Diatoms.Org: supporting taxonomists, connecting communities. Diatom Research. 36(4):291–304. doi:10.1080/0269249X.2021.2006790.
   Web of Science ®Google Scholar
• Stevenson, RJ, Y Pan. 1999. Assessing environmental conditions in rivers and streams with diatoms. In The diatoms – applications for the environmental and earth sciences edited bySmol, JP, Stoermer, EF ,11–40. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Venkataramanan, A, P Faure-Giovagnoli, C Regan, D Heudre, C Figus, P Usseglio-Polatera, C Pradalier, M Laviale. 2023. Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach. Eng Appl Artif Intell. 117:105594. doi:10.1016/j.engappai.2022.105594.
   Web of Science ®Google Scholar
• Whitton, BA, E Rott. 1996. Use of Algae for monitoring rivers II. Studia Student. G.m.b.H.
   Google Scholar
• Wishkerman, A, PB Hamilton. 2017. DiaCurv: a value-based curvature analysis application in diatom taxonomy. Diatom Research. 32(3):351–358. doi:10.1080/0269249X.2017.1368718.
   Web of Science ®Google Scholar