Cultural evolution relies on the social transmission of cultural traits along a population’s social network. Research indicates that network structure affects information spread and thus the capacity for cumulative culture. However, how network structure itself is driven by population-culture co-evolution remains largely unclear. We use a simple model to investigate how populations negotiate the trade-off between acquiring new skills and getting better at existing skills and how this trade-off shapes social networks. We find unexpected eco-evolutionary feedbacks from culture onto social networks and vice versa. We show that selecting for skill generalists results in sparse networks with diverse skill sets, whereas selecting for skill specialists results in dense networks and a population that specializes on the same few skills on which everyone is an expert. Our model advances our understanding of the complex feedbacks in cultural evolution and demonstrates how individual-level behavior can lead to the emergence of population-level structure.

Summary1. Estimating the similarity in space use (spatio-temporal home range overlap) of animals is important for many questions regarding behavioural ecology, wildlife management and conservation. The current methods that calculate joint space use generally do not account for proximity in space use, as all of them rely on the differences between the exact spatial overlay of utilization distributions, while spatial distances between distributions should be considered to truly quantify similarity. 2. We implemented the earth mover's distance (EMD), a spatially explicit method, that quantifies similarity between utilization distributions by calculating the effort it takes to shape one utilization distribution landscape into another, hence EMD. 3. The EMD is a method commonly used in image retrieval applications, and we propose its use to calculate similarity in space use in the framework of movement ecology. 4. We show that the EMD is a consistent and useful as well as versatile measure of overlap and provide an easy to use implementation in the R package move.

In nature, animals often ignore socially available information despite the multiple theoretical benefits of social learning over individual trial-and-error learning. Using information filtered by others is quicker, more efficient and less risky than randomly sampling the environment. To explain the mix of social and individual learning used by animals in nature, most models penalize the quality of socially derived information as either out of date, of poor fidelity or costly to acquire. Competition for limited resources, a fundamental evolutionary force, provides a compelling, yet hitherto overlooked, explanation for the evolution of mixed-learning strategies. We present a novel model of social learning that incorporates competition and demonstrates that (i) social learning is favoured when competition is weak, but (ii) if competition is strong social learning is favoured only when resource quality is highly variable and there is low environmental turnover. The frequency of social learning in our model always evolves until it reduces the mean foraging success of the population. The results of our model are consistent with empirical studies showing that individuals rely less on social information where resources vary little in quality and where there is high within-patch competition. Our model provides a framework for understanding the evolution of social learning, a prerequisite for human cumulative culture.