The Complexity of Social Complexity: A Quantitative Multidimensional Approach for Studies of Social Organization

摘要

The rapid increase in "big data" during the postgenomic era makes it crucial to appropriately measure the level of social complexity in comparative studies. We argue that commonly used qualitative classifications lump together species showing a broad range of social complexity and falsely imply that social evolution always progresses along a single linear stepwise trajectory that can be deduced from comparing extant species. To illustrate this point, we compared widely used social complexity measures in "primitively eusocial" bumble bees with "advanced eusocial" stingless bees, honey bees, and attine ants. We find that a single species can have both higher and lower levels of complexity compared with other taxa, depending on the social trait measured. We propose that measuring the complexity of individual social traits switches focus from semantic discussions and offers several directions for progress. First, quantitative social traits can be correlated with molecular, developmental, and physiological processes within and across lineages of social animals. This approach is particularly promising for identifying processes that influence or have been affected by social evolution. Second, key social complexity traits can be combined into multidimensional lineage-specific quantitative indices, enabling fine-scale comparison across species that are currently bundled within the same level of social complexity.