Author

A. Krellenstein

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

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 2 Approach:deep learning· 1 Assay:imaging· 1 Organism:multiple species· 1 Organism:non-human primate· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Houstin· 2 shared A. Winterl· 2 shared C. Le Bohec· 2 shared D. P. Zitterbart· 2 shared R. Cristofari· 2 shared T. Barracho· 2 shared A. Mainka· 1 shared B. Fabry· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersEcology

A. Houstin

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Author profile →

2 papers

Recent·Most cited

Remote sensing of emperor penguin abundance and breeding success

A. Winterl, S. Richter, A. Houstin, T. Barracho, M. Boureau, C. Cornec, D. Couet, R. Cristofari, C. Eiselt, B. Fabry, A. Krellenstein, C. Mark, A. Mainka, D. Menard, J. Morinay, , D. P. Zitterbart

This paper develops a phenological model to predict emperor penguin breeding pairs and chicks from sparse satellite data, improving population monitoring in remote areas.

bioRxiv · Aug 24, 2023 · PreprintEcology

RFIDeep: Unfolding the Potential of Deep Learning forRadio-Frequency Identification

G. Bardon, R. Cristofari, A. Winterl, T. Barracho, M. Benoiste, C. Ceresa, N. Chatelain, J. Courtecuisse, F. A. N. Fernandes, M. Gauthier-Clerc, J.-P. Gendner, Y. Handrich, A. Houstin, A. Krellenstein, N. Lecomte, , C. Le Bohec

RFIDeep uses deep learning to analyze RFID mark-recapture data and accurately determine breeding status, phenology, and success in seabirds.

bioRxiv · Mar 25, 2023Ecology