Author

M. Sievers

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 2 Biochemistry· 1 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 3 Approach:deep learning· 2 Assay:imaging· 2 Organism:zebrafish· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Approach:statistical model· 1

Frequent Co-Authors

C. J. Brown· 2 shared E. Ditria· 2 shared E. Jinks· 2 shared R. Connolly· 2 shared S. Lopez-Marcano· 2 shared B. J. Kauffman· 1 shared C. Buelow· 1 shared C. E. Lovelock· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBiochemistry

C. J. Brown

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Author profile →

4 papers

Recent·Most cited

From the Modern Synthesis to the Molecular Synthesis: updating how we teach and assess evolution by natural selection

M. Sievers, C. Reemts, K. Dickinson, J. Mukerji, I. B. Beltran, E. J. Theobald, V. Velasco, S. Freeman

This paper proposes reorganizing introductory biology courses to focus on the Molecular Synthesis, developing assessment tools to evaluate students' understanding of genotype-phenotype-fitness connections in evolution.

bioRxiv · Jul 19, 2021 · PreprintScientific Communication & Education

Future carbon emissions from global mangrove forest loss

M. F. Adame, R. M. Connolly, M. Turschwell, C. E. Lovelock, T. Fatoyinbo, D. Lagomasino, L. Goldberg, J. Holdorf, D. Friess, S. D. Sasmito, J. Sanderman, M. Sievers, C. Buelow, B. J. Kauffman, D. Bryan-Brown, C. J. Brown

This study projects future CO2 emissions from global mangrove forest loss and identifies key regions for mitigation strategies.

bioRxiv · Aug 27, 2020Biochemistry

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

E. Ditria, M. Sievers, R. Connolly, E. Jinks, S. Lopez-Marcano

This study trains deep learning models to detect luderick fish in coastal habitats, finding that models trained on multiple habitats perform better across different environments.

bioRxiv · May 19, 2020Ecology

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

E. Ditria, S. Lopez-Marcano, E. Jinks, M. Sievers, C. J. Brown, R. Connolly

This study demonstrates that deep learning outperforms human experts and citizen scientists in accurately and efficiently quantifying fish abundance from underwater footage.

bioRxiv · Oct 16, 2019Ecology