Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Signal evolution in prey recognition systems.

Marcio R Pie1

  • 1Departamento de Zoologia, Universidade Federal do Paraná, Caixa Postal 19020, 81530 Curitiba, PR, Brazil. pie@ufpr.br

Behavioural Processes
|January 11, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Where is the evidence for biodiversity equilibria?

Trends in ecology & evolution·2026
Same author

Phenotypic and Ecological Correlates of Population Decline in the World's Anurans.

Ecology and evolution·2026
Same author

A new species of Brachycephalus (Anura: Brachycephalidae) from Serra do Quiriri, northeastern Santa Catarina state, southern Brazil, with a review of the diagnosis among species of the B. pernix group and proposed conservation measures.

PloS one·2025
Same author

Earth history and trait innovation drive the global radiation of modern toads.

Proceedings. Biological sciences·2025
Same author

Ancient Introgression Explains Mitochondrial Genome Capture and Mitonuclear Discordance Among South American Collared Tropidurus Lizards.

Molecular ecology·2025
Same author

Global Diversity Patterns in Anurans Are Determined by Terrestrial and Arboreal Species.

Integrative zoology·2025
Same journal

Flexible Time-Series Analysis: A Dynamically Aware Method for Inferring Directed Dependencies in Behavioral Data.

Behavioural processes·2026
Same journal

Effects of group size and landmarks on escape behavior of three fish species.

Behavioural processes·2026
Same journal

Vocal individuality in two sympatric seabird species: The role of developmental strategy, analytical approach and sample size.

Behavioural processes·2026
Same journal

No evidence of sex-specific responses to chemosensory risk assessment cues in Harts rivulus.

Behavioural processes·2026
Same journal

Exploratory responses of rats to cage-mates and conspecifics from another cage in a habituation-dishabituation paradigm with multiple habituation stimuli.

Behavioural processes·2026
Same journal

Observation of drinking behaviour in the Ursus arctos marsicanus at a tree cavity (dendrotelm) in the central Apennines.

Behavioural processes·2026
See all related articles

This study adapts a kin recognition model to predator-prey dynamics. It suggests predators balance prey detection with avoiding false alarms by using different signals along a signal-to-noise axis.

Area of Science:

  • Behavioral Ecology
  • Evolutionary Biology
  • Theoretical Ecology

Background:

  • Predator-prey interactions involve complex signaling dynamics.
  • Existing models often simplify the trade-offs faced by predators in detecting prey versus avoiding false alarms.

Purpose of the Study:

  • To adapt a graphical model from kin recognition for analyzing predator-prey signaling.
  • To explore the concept of a signal-to-noise (S/N) axis for antipredation strategies.
  • To investigate the trade-offs between acceptance and rejection errors in prey recognition systems.

Main Methods:

  • Graphical modeling adapted from kin recognition studies.
  • Conceptualizing antipredation strategies along a signal-to-noise (S/N) continuum.
  • Analyzing error types: acceptance errors (mistaking background for prey) and rejection errors (missing actual prey).

Related Experiment Videos

Main Results:

  • Antipredation strategies are positioned on a signal-to-noise (S/N) axis, from concealing (low S/N) to conspicuous (high S/N).
  • Optimal prey recognition requires balancing acceptance and rejection errors.
  • The model predicts predator cue usage varies across the S/N axis.

Conclusions:

  • A unified graphical model can illuminate signaling in predator-prey systems.
  • Understanding the S/N axis is crucial for predator foraging efficiency and prey survival.
  • The model provides insights into the evolution of predator detection and prey evasion tactics.