Hierarchical networks of food exchange in the black garden antLasius niger

Abstract

In eusocial insects, the division of labour means, among other things, that only few individuals forage for the entire colony. The survival of the colony depends on their efficiency in fitting the nutritional needs of all its members. We aimed at measuring the network topology and at understanding the role and centrality of each caste in this network and as a consequence on food dissemination across castes. We constructed the trophallaxis networks from 34 food exchanges experiments-in black garden ants (Lasius niger). We tested the influence of brood and colony size on: (i) global indices at the network level, i.e. efficiency, resilience, centralisation and modularity; (ii) the individuals' values of degree, strength, betweenness and clustering coefficient. Network resilience, which is the ratio between global efficiency and centralisation, is stable with colony size but increases with the presence of a brood, in order to possibly respond to the needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some trophallactic network against resource quality.

Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms

“…Accepted Article Quque et al, 2020;Planckaert et al, 2019;Bles, Deneubourg, and Nicolis, 2018;Quevillon et al, 2015;Wikle, Hanks, and Hughes, 2019;Gernat et al, 2018;Choi et al, 2020). In the past, measuring food inside individual crops required to remove a sample of individuals from the colony (Sorensen, Busch, and Vinson, 1985;Howard and Tschinkel, 1980;Howard and Tschinkel, 1981b;Wilson and Eisner, 1957;Cassill and Tschinkel, 1999;Cassill and Tschinkel, 1996).…”

“…The second weakness of the previous imaging system (Greenwald, Segre, and Feinerman, 2015) is that, as in most studies on trophallactic networks (Sendova-Franks et al, 2010;Quque et al, 2020;Planckaert et al, 2019;Bles, Deneubourg, and Nicolis, 2018;Quevillon et al, 2015;Wikle, Hanks, and Hughes, 2019), it relied on manual detection of trophallactic events, which dramatically limits its throughput. Here, we present the first computer-vision based automatic ant trophallaxis detection algorithm.…”

Integrating real-time data analysis into automatic tracking of social insects

“…Although the technology underlying insect video tracking advances rapidly and significantly, many recently developed systems rely on offline data processing [11,[17][18][19][20][21]. This means that researchers often perform tracking on previously recorded videos and analyse data a posteriori.…”

“…Researchers usually employ tracking systems to analyse groups of ants moving in two-dimensional arenas, tagging each individual with unique identity markers (e.g. QR or ArUco matrix codes [ 7 , 8 , 11 , 12 ] or combinations of painted colour dots [ 4 – 6 , 13 ]) and using cameras to take images from top and/or bottom [ 14 ]. Many video trackers rely on inferring the position and orientation of insects via the detection of individual tags in images taken at given time intervals [ 7 , 12 ].…”

Integrating real-time data analysis into automatic tracking of social insect behavior

“…Automatic trackers can therefore easily scrape a significant amount of information just by scanning images of their colonies. Researchers usually employ tracking systems to analyze groups of ants moving in two-dimensional arenas, tagging each individual with unique identity markers (e.g., QR or ArUco matrix codes 7,8,11,12 or combinations of painted color dots [4][5][6]13 ) and using cameras to take images from top and/or bottom 14 . Many video trackers rely on inferring the position and orientation of insects via the detection of individual tags in images taken at given time intervals 7,12 .…”