Prabhleen Kaur

The paper uses an agent-based model to evaluate how sampling strategies affect the accuracy of social network metrics in animal studies, identifying optimal trade-offs between coverage, frequency, and duration.

The paper shows that random subsamples of animal populations can reliably reveal intrinsic social differences, highlighting the importance of accounting for sampling biases in social network analysis.

aniSNA assesses bias and uncertainty in animal social networks from telemetry data, enabling reliable network metrics and robust analysis.