Author

B. Spataro

Lyon 1 Université

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 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Organism:multiple species· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Simon· 1 shared C. Bonenfant· 1 shared C. Fraisse· 1 shared C. Mazoyer· 1 shared C. Roux· 1 shared D. Allaine· 1 shared E. Loire· 1 shared G. Dussert· 1 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperEvolutionary Biology

A. Simon

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A MammaLian Demographic Database for comparative analyses of evolutionary biodemography: malddaba

Victor Ronget, Jean‐François Lemaître, B. Spataro, Lionel Humblot, Jean‐Michel Gaillard

The paper introduces malddaba, a database compiling age-specific demographic rates for mammals, enabling comparative analyses of survival and reproduction patterns across species.

bioRxiv · Jul 1, 2025 · PreprintEcology

DILS : Demographic Inferences with Linked Selection by using ABC

C. Fraisse, I. Popovic, C. Mazoyer, B. Spataro, S. Delmotte, J. Romiguier, E. Loire, A. Simon, N. Galtier, L. Duret, N. Bierne, X. Vekemans, C. Roux

DILS performs demographic inferences with linked selection using ABC on population genomic data, identifying models and loci associated with gene flow and population size changes.

bioRxiv · Jun 15, 2020Evolutionary Biology

Revisiting giraffe photo-identification using deep learning and network analysis

V. Miele, G. Dussert, B. Spataro, S. Chamaille-Jammes, D. Allaine, C. Bonenfant

The paper develops a deep learning pipeline for giraffe re-identification using image similarity networks and CNNs, achieving 90% Top-1 accuracy in identifying known individuals from 4,000 photographs.

bioRxiv · Mar 25, 2020 · PreprintEcology