Collective behaviors efficiently impart benefits to a diversity of species ranging from bacteria to humans. Fly larvae tend to cluster and form coordinated digging groups under crowded conditions, yet understanding the rules governing this behavior is in its infancy. We primarily took advantage of the Drosophila model to investigate cooperative foraging behavior. Here, we report that Drosophila-related species and the black soldier fly have evolved a conserved strategy of cluster digging in food foraging. Subsequently, we investigated relative factors, including larval stage, population density, and food stiffness and quality, that affect the cluster digging behavior. Remarkably, oxygen supply through the posterior breathing spiracles is necessary for the organization of digging clusters. More importantly, we theoretically devise a mathematical model to accurately calculate how the cluster digging behavior expands food resources by diving depth, cross-section area, and food volume. We found that cluster digging behavior approximately increases 2.2 fold depth, 1.7-fold cross-section area, and 1.9 fold volume than control groups, respectively. Amplification of food sources significantly facilitates survival, larval development, and reproductive success of Drosophila challenged with competition for limited food resources, thereby conferring trophic benefits to fitness in insects. Overall, our findings highlight that the cluster digging behavior is a pivotal behavior for their adaptation to food scarcity, advancing a better understanding of how this cooperative behavior confers fitness benefits in the animal kingdom.
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http://dx.doi.org/10.1111/1744-7917.13307 | DOI Listing |
Mol Phylogenet Evol
October 2024
Department of Zoology, Faculty of Sciences, University of South Bohemia in České Budějovice, Czech Republic; Institute of Vertebrate Biology, Czech Academy of Sciences, Brno, Czech Republic.
Phys Rev E
June 2024
Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China.
Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks.
View Article and Find Full Text PDFSci Rep
January 2024
Laboratoire de Physique Théorique et Modélisation, UMR-8089 CNRS, CY Cergy Paris Université, Cergy-Pontoise, France.
We present an extensive study of the joint effects of heterogeneous social agents and their heterogeneous social links in a bounded confidence opinion dynamics model. The full phase diagram of the model is explored for two different network's topologies and compared to two opposed extreme cases: on one hand, the heterogeneous agents constitute a mixed population and on the other, their interactions are modeled by a lattice. The results show that when agents prone to compromise coexist with close-minded ones, the steady state of the dynamics shows coexistent phases.
View Article and Find Full Text PDFInsect Sci
June 2024
School of Plant Protection, Anhui Agricultural University; Anhui Province Key Laboratory of Crop Integrated Pest Management, Anhui Province Engineering Laboratory for Green Pesticide Development and Application, Hefei, China.
Collective behaviors efficiently impart benefits to a diversity of species ranging from bacteria to humans. Fly larvae tend to cluster and form coordinated digging groups under crowded conditions, yet understanding the rules governing this behavior is in its infancy. We primarily took advantage of the Drosophila model to investigate cooperative foraging behavior.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!