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Modelling nutrition across organizational levels: from individuals to superorganisms

Citation

Lihoreau, M and Buhl, J and Charleston, MA and Sword, GA and Raubenheimer, D and Simpson, SJ, Modelling nutrition across organizational levels: from individuals to superorganisms, Journal of Insect Physiology, 69 pp. 2-11. ISSN 0022-1910 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.jinsphys.2014.03.004

Abstract

The Geometric Framework for nutrition has been increasingly used to describe how individual animals regulate their intake of multiple nutrients to maintain target physiological states maximizing growth and reproduction. However, only a few studies have considered the potential influences of the social context in which these nutritional decisions are made. Social insects, for instance, have evolved extreme levels of nutritional interdependence in which food collection, processing, storage and disposal are performed by different individuals with different nutritional needs. These social interactions considerably complicate nutrition and raise the question of how nutrient regulation is achieved at multiple organizational levels, by individuals and groups. Here, we explore the connections between individual- and collective-level nutrition by developing a modelling framework integrating concepts of nutritional geometry into individual-based models. Using this approach, we investigate how simple nutritional interactions between individuals can mediate a range of emergent collective-level phenomena in social arthropods (insects and spiders) and provide examples of novel and empirically testable predictions. We discuss how our approach could be expanded to a wider range of species and social systems.

Item Details

Item Type:Refereed Article
Keywords:collective behaviour, division of labour, geometric framework, individual-based model, nutritional ecology, social arthropods
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Population, Ecological and Evolutionary Genetics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Biological Sciences
Author:Charleston, MA (Associate Professor Michael Charleston)
ID Code:109285
Year Published:2014
Web of Science® Times Cited:17
Deposited By:Mathematics and Physics
Deposited On:2016-06-07
Last Modified:2017-12-08
Downloads:85 View Download Statistics

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