Joanna Chang, Scott Powell, Elva J. H. Robinson, Matina C. Donaldson-Matasci
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Here, we ask how a turtle ant colony’s choice of which nests to include in a network may be influenced by their potential to create connections to other nests. In laboratory experiments with <i>Cephalotes varians</i> and <i>Cephalotes texanus</i>, we show that nest choice is influenced by spatial constraints, but in unexpected ways. Under one spatial configuration, colonies preferentially occupied more connected nest sites; however, under another spatial configuration, this preference disappeared. Comparing the results of these experiments to an agent-based model, we demonstrate that this apparently idiosyncratic relationship between nest connectivity and nest choice can emerge without nest preferences via a combination of self-reinforcing random movement along constrained pathways and density-dependent aggregation at nests. While this mechanism does not consistently lead to the de-novo construction of low-cost, efficient transport networks, it may be an effective way to expand a network, when coupled with processes of pruning and restructuring.</p>","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"22 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2021-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Nest choice in arboreal ants is an emergent consequence of network creation under spatial constraints\",\"authors\":\"Joanna Chang, Scott Powell, Elva J. H. Robinson, Matina C. Donaldson-Matasci\",\"doi\":\"10.1007/s11721-021-00187-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Biological transportation networks must balance competing functional priorities. The self-organizing mechanisms used to generate such networks have inspired scalable algorithms to construct and maintain low-cost and efficient human-designed transport networks. The pheromone-based trail networks of ants have been especially valuable in this regard. Here, we use turtle ants as our focal system: In contrast to the ant species usually used as models for self-organized networks, these ants live in a spatially constrained arboreal environment where both nesting options and connecting pathways are limited. Thus, they must solve a distinct set of challenges which resemble those faced by human transport engineers constrained by existing infrastructure. Here, we ask how a turtle ant colony’s choice of which nests to include in a network may be influenced by their potential to create connections to other nests. In laboratory experiments with <i>Cephalotes varians</i> and <i>Cephalotes texanus</i>, we show that nest choice is influenced by spatial constraints, but in unexpected ways. Under one spatial configuration, colonies preferentially occupied more connected nest sites; however, under another spatial configuration, this preference disappeared. Comparing the results of these experiments to an agent-based model, we demonstrate that this apparently idiosyncratic relationship between nest connectivity and nest choice can emerge without nest preferences via a combination of self-reinforcing random movement along constrained pathways and density-dependent aggregation at nests. While this mechanism does not consistently lead to the de-novo construction of low-cost, efficient transport networks, it may be an effective way to expand a network, when coupled with processes of pruning and restructuring.</p>\",\"PeriodicalId\":51284,\"journal\":{\"name\":\"Swarm Intelligence\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2021-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11721-021-00187-5\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11721-021-00187-5","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Nest choice in arboreal ants is an emergent consequence of network creation under spatial constraints
Biological transportation networks must balance competing functional priorities. The self-organizing mechanisms used to generate such networks have inspired scalable algorithms to construct and maintain low-cost and efficient human-designed transport networks. The pheromone-based trail networks of ants have been especially valuable in this regard. Here, we use turtle ants as our focal system: In contrast to the ant species usually used as models for self-organized networks, these ants live in a spatially constrained arboreal environment where both nesting options and connecting pathways are limited. Thus, they must solve a distinct set of challenges which resemble those faced by human transport engineers constrained by existing infrastructure. Here, we ask how a turtle ant colony’s choice of which nests to include in a network may be influenced by their potential to create connections to other nests. In laboratory experiments with Cephalotes varians and Cephalotes texanus, we show that nest choice is influenced by spatial constraints, but in unexpected ways. Under one spatial configuration, colonies preferentially occupied more connected nest sites; however, under another spatial configuration, this preference disappeared. Comparing the results of these experiments to an agent-based model, we demonstrate that this apparently idiosyncratic relationship between nest connectivity and nest choice can emerge without nest preferences via a combination of self-reinforcing random movement along constrained pathways and density-dependent aggregation at nests. While this mechanism does not consistently lead to the de-novo construction of low-cost, efficient transport networks, it may be an effective way to expand a network, when coupled with processes of pruning and restructuring.
期刊介绍:
Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research
and developments in the multidisciplinary field of swarm intelligence. The journal publishes
original research articles and occasional review articles on theoretical, experimental and/or
practical aspects of swarm intelligence. All articles are published both in print and in electronic
form. There are no page charges for publication. Swarm Intelligence is published quarterly.
The field of swarm intelligence deals with systems composed of many individuals that coordinate
using decentralized control and self-organization. In particular, it focuses on the collective
behaviors that result from the local interactions of the individuals with each other and with their
environment. It is a fast-growing field that encompasses the efforts of researchers in multiple
disciplines, ranging from ethology and social science to operations research and computer
engineering.
Swarm Intelligence will report on advances in the understanding and utilization of swarm
intelligence systems, that is, systems that are based on the principles of swarm intelligence. The
following subjects are of particular interest to the journal:
• modeling and analysis of collective biological systems such as social insect colonies, flocking
vertebrates, and human crowds as well as any other swarm intelligence systems;
• application of biological swarm intelligence models to real-world problems such as distributed
computing, data clustering, graph partitioning, optimization and decision making;
• theoretical and empirical research in ant colony optimization, particle swarm optimization,
swarm robotics, and other swarm intelligence algorithms.