Impact of deep learning and post-processing algorithms performances on biodiversity metrics assessed on videos
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PLOS One | https://doi.org/10.1371/journal.pone.0327577 August 11, 2025 13 / 14 confidence scores to minimize misidentifications. The Jaccard similarity index was used to measure the difference in com- position of species present on the videos and the composition estimated after automated processing using algorithms. (TIF) Acknowledgments The authors thank the two anonymous reviewers who helped improve and clarify this manuscript. Author contributions Conceptualization: Valentine Fleuré, Sébastien Villéger. Methodology: Valentine Fleuré. Visualization: Valentine Fleuré. Writing – original draft: Valentine Fleuré. Writing – review & editing: Valentine Fleuré, Kévin Planolles, Thomas Claverie, Baptiste Mulot, Sébastien Villéger. References 1. Bush A, Sollmann R, Wilting A, Bohmann K, Cole B, Balzter H, et al. Connecting Earth observation to high-throughput biodiversity data. Nat Ecol Evol. 2017;1(7):1–9. 2. Sparrow BD, Edwards W, Munroe SEM, Wardle GM, Guerin GR, Bastin J-F, et al. Effective ecosystem monitoring requires a multi-scaled approach. Biol Rev Camb Philos Soc. 2020;95(6):1706–19. https://doi.org/10.1111/brv.12636 PMID: 32648358 3. Spalding M. World atlas of coral reefs. 2001. 4. Trenkel V, Vaz S, Albouy C, Brind’Amour A, Erwan D, Laffargue P, et al. We can reduce the impact of scientific trawling on marine ecosystems. Marine Ecol Progress Series. 2018;609. 5. Cristescu ME, Hebert PDN. Uses and misuses of environmental DNA in biodiversity science and conservation. Ann Rev Ecol Evol Syst. 2018;49:209–30.
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