Author

F. A. Francisco

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

Biology Disciplines

Click to filter author papers

Animal Behavior & Cognition· 2 Ecology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:experimental assay· 2 Assay:behavioral assay· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:ecology & biodiversity· 1 Assay:imaging· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

D. Bierbach· 2 shared J. Lukas· 2 shared A. Escurra Alegre· 1 shared A. Jordan· 1 shared A. Stöcker· 1 shared C. Schutz· 1 shared D. Hof· 1 shared F. W. Miesen· 1 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersAnimal Behavior & Cognition

D. Bierbach

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Experienced Social Partners Hinder Learning Performance in Naive Clonal Fish

F. A. Francisco, A. Stöcker, J. Lukas, P. Romanczuk, D. Bierbach

This study shows that experienced social partners can hinder learning performance in naive clonal fish, challenging the assumption that social learning always enhances learning efficiency.

bioRxiv · Oct 13, 2022 · PreprintAnimal Behavior & Cognition

Discovery of a new but established population of the guppy in Germany

D. Bierbach, C. Schutz, N. Weimar, A. Escurra Alegre, F. A. Francisco, S. Wersing, O. L. O'Connor, M. Kempkes, U. Rose, F. W. Miesen, M. Habedank, J. Jourdan, J. Kabus, D. Hof, S. Hornung, , J. Lukas

The paper describes a newly discovered established population of guppies in a thermally polluted river in Germany, including population size estimates and parasitism observations.

bioRxiv · Feb 1, 2022 · PreprintEcology

A low-cost, open-source framework for tracking and behavioural analysis of animals in aquatic ecosystems

F. A. Francisco, P. Nuehrenberg, A. Jordan

This paper introduces a low-cost, open-source framework using deep learning and structure-from-motion to track and analyze animal behavior in aquatic environments with high accuracy and minimal infrastructure.

bioRxiv · Mar 9, 2019 · PreprintAnimal Behavior & Cognition