Beyond the Serengeti lion: addressing ecological and systemic biases in behavioural research through horizontal comparison

Long-term studies of wild animal populations have played a central role in behavioural ecology by enabling detailed, longitudinal analyses across individuals and groups. These systems have generated foundational insights into the proximate and ultimate drivers of behaviour, and some, like the Serengeti lion population, have come to function as ‘model systems’. While such systems offer exceptional depth, they may also limit our understanding of species-wide variation when findings are generalized from the context of a single or few populations. Drawing on our collective experience of studying multiple lion populations, we critically examine how ecological variability, methodological bias and representational gaps have shaped prevailing assumptions about lion social behaviour derived largely from the Serengeti population. We identify key areas where lion social structure, group dynamics and reproductive strategies diverge from the Serengeti model, and highlight how foundational knowledge can often be constrained by geography, research history and sampling bias. Similar patterns are also evident in other well-studied species where ecobehavioural paradigms are built from limited population studies. In this paper, we argue for a more comparative, cross-population approach to animal behaviour research that integrates diverse knowledge models and voices; one that embraces ecological and epistemic diversity as informative rather than anomalous. Such an approach, empowered by integrating new technology with local knowledge, can deepen our understanding of behavioural plasticity and broaden the theoretical foundations of the discipline. • Lion social patterns vary widely and Serengeti lions are not fully representative. • Single-population studies risk biased assumptions about behaviour and reproduction. • Comparing multiple populations reveals ecological and behavioural diversity. • Integrating technology and local knowledge can broaden behavioural theory.