Projects per year
Abstract
Recent years have seen an explosion of multidisciplinary interest in ancient human warfare. Theory has emphasised a key role for kin-selected cooperation, modulated by sex-specific demography, in explaining intergroup violence. However, conflicts of interest remain a relatively underexplored factor in the evolutionary-ecological study of warfare, with little consideration given to which parties influence the decision to go to war and how their motivation may differ. We develop a mathematical model to investigate the interplay between sex-specific demography and human warfare, showing that: the ecology of warfare drives the evolution of sex-biased dispersal; sex-biased dispersal modulates intrafamily and intragenomic conflicts in relation to warfare; intragenomic conflict drives parent-of-origin-specific patterns of gene expression – i.e. 'genomic imprinting' – in relation to warfare phenotypes; and an ecological perspective of conflicts at the levels of the gene, individual and social group yields novel predictions as to pathologies associated with mutations and epimutations at loci underpinning human violence.
Original language | English |
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Article number | 20162699 |
Number of pages | 10 |
Journal | Proceedings of the Royal Society B: Biological Sciences |
Volume | 284 |
Issue number | 1849 |
DOIs | |
Publication status | Published - 22 Feb 2017 |
Keywords
- War
- Sex-biased dispersal
- Parent-offspring conflict
- Sexual conflict
- Intragenomic conflict
- Genomic imprinting
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Dive into the research topics of 'Intrafamily and intragenomic conflicts in human warfare'. Together they form a unique fingerprint.Projects
- 1 Finished
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NERC Fellowship: Understanding major transitions in individuality
Gardner, A. (PI)
31/03/14 → 30/04/22
Project: Standard
Profiles
Datasets
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Supplementary material from "Intrafamily and intragenomic conflicts in human warfare"
Micheletti, A. J. C. (Creator), Ruxton, G. D. (Supervisor) & Gardner, A. (Supervisor), The Royal Society, 8 Feb 2017
DOI: 10.6084/m9.figshare.c.3685606.v1, http://hdl.handle.net/10023/17494
Dataset: Thesis dataset