Davis, N. and Jarvis, A. and Polhill, J.G.
(2022)
Co-evolution of network structure and consumer inequality in a spatially explicit model of energetic resource acquisition.
Physica A: Statistical Mechanics and its Applications, 608 (Part 1).
ISSN 0378-4371
Full text not available from this repository.
Abstract
Energetic resources in ecological and social–ecological systems are distributed through complex networks, which co-evolve with the system and consumers to move resources from points of origin to those of end use. Past research has focused on effects of spatiotemporal resource heterogeneity in ecosystems and society, or socioeconomic drivers of inequality, with less attention to interactions between resource network structure and population-level outcomes. Here, we develop a spatially explicit, stock-flow consistent agent-based model of generic consumers building and crossing links between resources, and we explore the co-evolution of the emergent network structure and inequality in consumers’ resource reserves across three distinct landscapes. We show that the consumer inequality initially decreased during network expansion, then increased rapidly as the network reached a more stable state. The spatial distribution of resources in each of the three landscapes constrained the structures that could emerge, and therefore the specific rates and timings of these dynamics. This work demonstrates the use of energetically consistent modelling to understand possible relationships among a spatially distributed set of resources, the network structure that connects them to a population, and inequality in that population. This can provide a theoretical underpinning informing further work to better understand causes of resource inequality and heterogeneity in observed systems.
Item Type:
Journal Article
Journal or Publication Title:
Physica A: Statistical Mechanics and its Applications
Additional Information:
Export Date: 23 November 2022 Funding details: JHI-C5-1, UK 2022–27 Funding details: James Hutton Institute Funding details: Biotechnology and Biological Sciences Research Council, BBSRC, BB/S019669/1 Funding details: National Institute of Agricultural Biotechnology, NIAB Funding text 1: Funding was provided by a joint Lancaster University/The James Hutton Institute, UK Ph.D. studentship to ND. The authors acknowledge useful comments from two anonymous reviewers, which helped sharpen the manuscript, and the efforts of the editor, Dr Michael Small, in finding said reviewers. In addition, ND acknowledges statistical advice from Dr Vicki Davis and discussions on the modelling framework with Dr Kirsti Ashworth, as well as feedback on model and experimental design from Dr Nanda Wijermans and Dr Émile Chappin at ESSA@work during Social Simulation Week 2020. The authors also acknowledge the Research/Scientific Computing teams at The James Hutton Institute and NIAB for providing computational resources and enduringly patient technical support for the “UK’s Crop Diversity Bioinformatics HPC” ( BBSRC, UK grant BB/S019669/1 ) and the James Hutton Institute computing cluster, which were used to run the simulations reported within this paper. GP is grateful for funding from the Scottish Government’s Strategic Research Programme, UK 2022–27 (project JHI-C5-1 ). Funding text 2: Funding was provided by a joint Lancaster University/The James Hutton Institute, UK Ph.D. studentship to ND. The authors acknowledge useful comments from two anonymous reviewers, which helped sharpen the manuscript, and the efforts of the editor, Dr Michael Small, in finding said reviewers. In addition, ND acknowledges statistical advice from Dr Vicki Davis and discussions on the modelling framework with Dr Kirsti Ashworth, as well as feedback on model and experimental design from Dr Nanda Wijermans and Dr Émile Chappin at ESSA@work during Social Simulation Week 2020. The authors also acknowledge the Research/Scientific Computing teams at The James Hutton Institute and NIAB for providing computational resources and enduringly patient technical support for the “UK's Crop Diversity Bioinformatics HPC” (BBSRC, UK grant BB/S019669/1) and the James Hutton Institute computing cluster, which were used to run the simulations reported within this paper. GP is grateful for funding from the Scottish Government's Strategic Research Programme, UK 2022–27 (project JHI-C5-1).
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3104
Subjects:
?? STATISTICS AND PROBABILITYCONDENSED MATTER PHYSICS ??
Deposited On:
30 Nov 2022 14:25
Last Modified:
18 Sep 2023 02:09