MacaqueNet: big-team research into the biological drivers of social relationships

Abstract

AbstractFor many animals, social relationships are a key determinant of fitness. However, major gaps remain in our understanding of the adaptive function, ontogeny, evolution, and mechanistic underpinnings of social relationships. There is a vast and ever-accumulating amount of social behavioural data on individually recognised animals, an incredible resource to shed light onto the biological basis of social relationships. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible, and standardised databases.Here, we advocate for the creation of big-team collaborations and comparative databases to unlock the wealth of behavioural data for research on social relationships by introducing MacaqueNet (https://macaquenet.github.io/).As a global collaboration of over 100 researchers, the MacaqueNet database encompasses data from 1981 to the present on 14 species and is the first publicly searchable and standardised database on affiliative and agonistic animal social networks. With substantial inter-specific variation in ecology and social structure and the first published record on macaque behaviour dating back to 1956, macaque research has already contributed to answering fundamental questions on the biological bases and evolution of social relationships. Building on these strong foundations, we believe that MacaqueNet can further promote collaborative and comparative research on social behaviour.We believe that big-team approaches to building standardised databases, bringing together data contributors and researchers, will aid much-needed large-scale comparative research in behavioural ecology and beyond. We describe the establishment of MacaqueNet, from starting a large-scale collective to the creation of a cross-species collaborative database and the implementation of data entry and retrieval protocols. As such, we hope to provide a functional example for future endeavours of large-scale collaborative research into the biology of social behaviour.