Ants adapt in surprising ways to extreme environments, according to an international study that analyzed social patterns of over 3,000 species. Led by the University of Lausanne in collaboration with the University of Hong Kong, the work highlights how these insects have conquered almost all terrestrial habitats, except Antarctica.
Key social patterns in global colonization
Published in Proceedings of the National Academy of Sciences, the research used genetic and ecological data to map the diversity of ants adapt through varied social structures. In cold regions, large colonies with strict division of labor prevail; in deserts, nomadic strategies allow survival with scarce resources. These findings explain their presence in forests, mountains, and cities.
Historical context and verified data
Since the 1970s, entomologists like E.O. Wilson have documented ant supercolonization, with current estimates of 12,000 to 22,000 known species. This study expands those works by integrating modern genomics, confirming that ants adapt by evolving behaviors such as fungal agriculture in tropical American species or slavery in European species. Experts like Professor Laurent Keller of Lausanne assert: “Ants dominate the Earth thanks to their social flexibility”.
Implications for science and climate change
Ants represent 20% of terrestrial animal biomass, influencing soils and seed dispersal. As the planet warms, understanding how ants adapt helps predict ecological impacts. In the Dominican Republic, local species like the leaf-cutting ant already show resilience in altered coffee plantations.
- Over 3,000 species analyzed in the study.
- Successful colonization in 99% of terrestrial habitats.
- Social strategies: massive colonies, nomadic, or parasitic.
This advance underscores the biological engineering of ants. Researchers call for more studies in underrepresented regions to delve deeper into how ants adapt to human threats. In evolutionary biology, these insects remain a model of planetary success, inspiring robotics and optimization algorithms.
