Multidimensional social influence drives leadership and composition-dependent success in octopus–fish hunting groups
Year: 2024
Type: article
Abstract: Collective behaviour, social interactions and leadership in animal groups are often driven by individual differences. However, most studies focus on same-species groups, in which individual variation is relatively low. Multispecies groups, however, entail interactions among highly divergent phenotypes, ranging from simple exploitative actions to complex coordinated networks. Here we studied hunting groups of otherwise-solitary Octopus cyanea and multiple fish species, to unravel hidden mechanisms of leadership and associated dynamics in functional nature and complexity, when divergence is maximized. Using three-dimensional field-based tracking and field experiments, we found that these groups exhibit complex functional dynamics and composition-dependent properties. Social influence is hierarchically distributed over multiscale dimensions representing role specializations: fish (particularly goatfish) drive environmental exploration, deciding where, while the octopus decides if, and when, the group moves. Thus, 'classical leadership' can be insufficient to describe complex heterogeneous systems, in which leadership instead can be driven by both stimulating and inhibiting movement. Furthermore, group composition altered individual investment and collective action, triggering partner control mechanisms (that is, punching) and benefits for the de facto leader, the octopus. This seemingly non-social invertebrate flexibly adapts to heterospecific actions, showing hallmarks of social competence and cognition. These findings expand our current understanding of what leadership is and what sociality is.
Source: Nature Ecology & Evolution
Language: English
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
Funders: U.S. Department of Defense, National Geographic Society, Animal Behavior Society, European Commission, Deutsche Forschungsgemeinschaft, Magyar Tudományos Akadémia, Gottfried Wilhelm Leibniz Universität Hannover, Max-Planck-Gesellschaft, Universiteit Gent, Eötvös Loránd Tudományegyetem, Universität Konstanz, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Magyar Tudományos Akadémia Számítástechnikai és Automatizálási Kutatóintézet, Leibniz-Gemeinschaft, Fundação para a Ciência e a Tecnologia, Office of Naval Research, Division of Ocean Sciences, Office of Naval Research Global
Awards: G1871071817, MARE - Centro de Ciências do Mar e do Ambiente, G203253094, G2038046918, G2088408388, G2381975105, G2463333092, Advanced simulation, analysis and interpretation of network structures in biological data, G434337789, G5454278309, G6032066843, G7175315767, G771654109, G8188088470, G8677317511, G8858051513, G8876996369
OpenAlex ID: W4402745910
