S
Sebastiano De Bona
Researcher at University of Jyväskylä
Publications - 11
Citations - 182
Sebastiano De Bona is an academic researcher from University of Jyväskylä. The author has contributed to research in topics: Population & Biology. The author has an hindex of 5, co-authored 8 publications receiving 125 citations.
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Journal ArticleDOI
Deimatism: a neglected component of antipredator defence.
Kate D. L. Umbers,Sebastiano De Bona,Thomas E. White,Jussi Lehtonen,Johanna Mappes,John A. Endler +5 more
TL;DR: The differences among deimatism, aposematism, and forms of mimicry, and their ecological and evolutionary implications are discussed, and outstanding questions critical to progress are highlighted.
Journal ArticleDOI
Predator mimicry, not conspicuousness, explains the efficacy of butterfly eyespots.
Sebastiano De Bona,Janne K. Valkonen,Andrés López-Sepulcre,Andrés López-Sepulcre,Johanna Mappes +4 more
TL;DR: The eye-mimicry hypothesis explains the results better than the conspicuousness hypothesis and is thus likely to be an important mechanism behind the evolution of butterfly eyespots.
Journal ArticleDOI
Spatio-temporal dynamics of density-dependent dispersal during a population colonisation.
Sebastiano De Bona,Matthieu Bruneaux,Alex E. G. Lee,David N. Reznick,Paul Bentzen,Andrés López-Sepulcre,Andrés López-Sepulcre +6 more
TL;DR: It is demonstrated that densities at various scales interact to determine dispersal, and suggests that dispersal trade-offs differ across life stages, as well as the importance of habitat quality and competition over resources.
Journal ArticleDOI
The protective value of a defensive display varies with the experience of wild predators.
Kate D. L. Umbers,Thomas E. White,Sebastiano De Bona,Tonya M. Haff,Julia Ryeland,Eleanor Drinkwater,Johanna Mappes +6 more
TL;DR: The results suggest that deimatism does not require predator learning to afford protection, but that a predator can learn to expect the display and subsequently avoid it or ignore it.
Journal ArticleDOI
Item Response Trees: a recommended method for analyzing categorical data in behavioral studies
Andrés López-Sepulcre,Andrés López-Sepulcre,Sebastiano De Bona,Janne K. Valkonen,Kate D. L. Umbers,Johanna Mappes +5 more
TL;DR: It is argued that instead of being analyzed using traditional nonparametric statistics or a series of separate analyses split by response categories, this kind of data can be more holistically analyzed using a generalized linear mixed model (GLMM) framework extended to binomial response trees.