To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A computational neuroscience framework for quantifying warning signals
School of Psychology and Neuroscience University of St Andrews, St Andrews Fife UK; Computer Science Department, Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain.ORCID iD: 0000-0002-1544-2405
Centre for Behaviour and Evolution Newcastle University Biosciences Institute, Newcastle University, Newcastle upon Tyne UK.
School of Biological Sciences, University of Bristol, Bristol, UK.ORCID iD: 0000-0002-5007-8856
Division of Psychology and Forensic Sciences, School of Applied Sciences, Abertay University, Dundee, UK.ORCID iD: 0000-0003-2959-5370
Show others and affiliations
2023 (English)In: Methods in Ecology and Evolution, E-ISSN 2041-210X, Vol. 15, no 1, p. 103-116Article in journal (Refereed) Published
Abstract [en]

1. Animal warning signals show remarkable diversity, yet subjectively appear to share certain visual features that make defended prey stand out and look different from more cryptic palatable species. For example, many (but far from all) warning signals involve high contrast elements, such as stripes and spots, and often involve the colours yellow and red. How exactly do aposematic species differ from non-aposematic ones in the eyes (and brains) of their predators?

2. Here, we develop a novel computational modelling approach, to quantify prey warning signals and establish what visual features they share. First, we develop a model visual system, made of artificial neurons with realistic receptive fields, to provide a quantitative estimate of the neural activity in the first stages of the visual system of a predator in response to a pattern. The system can be tailored to specific species. Second, we build a novel model that defines a ‘neural signature’, comprising quantitative metrics that measure the strength of stimulation of the population of neurons in response to patterns. This framework allows us to test how individual patterns stimulate the model predator visual system.

3. For the predator–prey system of birds foraging on lepidopteran prey, we compared the strength of stimulation of a modelled avian visual system in response to a novel database of hyperspectral images of aposematic and undefended butterflies and moths. Warning signals generate significantly stronger activity in the model visual system, setting them apart from the patterns of undefended species. The activity was also very different from that seen in response to natural scenes. Therefore, to their predators, lepidopteran warning patterns are distinct from their non-defended counterparts and stand out against a range of natural backgrounds.

4. For the first time, we present an objective and quantitative definition of warning signals based on how the pattern generates population activity in a neural model of the brain of the receiver. This opens new perspectives for understanding and testing how warning signals have evolved, and, more generally, how sensory systems constrain signal design.

Place, publisher, year, edition, pages
British Ecological Society, 2023. Vol. 15, no 1, p. 103-116
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:oru:diva-115795DOI: 10.1111/2041-210x.14268ISI: 001121340900001OAI: oai:DiVA.org:oru-115795DiVA, id: diva2:1895532
Available from: 2024-09-05 Created: 2024-09-05 Last updated: 2025-01-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Halpin, Christina G.

Search in DiVA

By author/editor
Penacchio, O.Halpin, Christina G.Cuthill, I. C.Lovell, P. G.Skelhorn, J.Rowe, C.Harris, J. M.
In the same journal
Methods in Ecology and Evolution
Biological Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 12 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf