Individuals from different-looking animal species may group together to confuse shared predators: simulations with artificial neural networks

Tosh, C. R., Jackson, A. L. and Ruxton, G. D. (2007) Individuals from different-looking animal species may group together to confuse shared predators: simulations with artificial neural networks. Proceedings of the Royal Society of London Series B: Biological Sciences, 274(1611), pp. 827-832. (doi: 10.1098/rspb.2006.3760) (PMID:17251090) (PMCID:PMC2093981)

Abstract

Individuals of many quite distantly related animal species find each other attractive and stay together for long periods in groups. We present a mechanism for mixed-species grouping in which individuals from different-looking prey species come together because the appearance of the mixed-species group is visually confusing to shared predators. Using an artificial neural network model of retinotopic mapping in predators, we train networks on random projections of single- and mixed-species prey groups and then test the ability of networks to reconstruct individual prey items from mixed-species groups in a retinotopic map. Over the majority of parameter space, cryptic prey items benefit from association with conspicuous prey because this particular visual combination worsens predator targeting of cryptic individuals. However, this benefit is not mutual as conspicuous prey tends to be targeted most poorly when in same-species groups. Many real mixed-species groups show the asymmetry in willingness to initiate and maintain the relationship predicted by our study. The agreement of model predictions with published empirical work, the efficacy of our modelling approach in previous studies, and the taxonomic ubiquity of retinotopic maps indicate that we may have uncovered an important, generic selective agent in the evolution of mixed-species grouping.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ruxton, Professor Graeme and Tosh, Dr Colin
• Authors: Tosh, C. R., Jackson, A. L., and Ruxton, G. D.
• Subjects: Q Science > QH Natural history > QH301 Biology; Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Medical Veterinary and Life Sciences
• Journal Name: Proceedings of the Royal Society of London Series B: Biological Sciences
• ISSN: 0962-8452
• ISSN (Online): 1471-2954

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
36420	1	A general neural-network model of the cognitive basis for the confusion effect	Graeme Ruxton	Biotechnology and Biological Sciences Research Council (BBSRC)	BBS/B/01790	Institute of Biodiversity Animal Health and Comparative Medicine