What we do
The Behavioral Complexity Lab studies how animals sense, communicate, and make decisions by focusing on the causal processes that generate ecological and social patterns in the wild. We pair intensive fieldwork with transparent, reproducible workflows to track how information moves through ecosystems and link environmental constraints, social interactions, organismal traits, and observation processes to behavior, biodiversity, and community organization. This work is organized around a set of approaches that are designed to push ecological research toward clearer, mechanism-based inference (rather than muddled ecological inference):
- Structural causal modeling (SCMs) and DAG-based workflows that focus on explicit, estimand-driven questions
- Novel field-deployable sensor systems that expand what can be measured in natural systems and bring behavior and environment into the same causal framework
- Information-centric approaches that treat signals, cues, and soundscapes as causal drivers of behavior and ecological interactions
- AI-assisted and computational methods that scale the extraction and synthesis of complex behavioral and acoustic data for causal analysis
- Estimand-driven workflows and re-analysis of ecological studies that move the field away from undirected “kitchen-sink” models and clarify how causal assumptions shape ecological theory