Let's DAG in: how directed acyclic graphs can help behavioural ecology be more transparent

Area of Science:

• Ecology
• Evolutionary Biology
• Causal Inference

Background:

• Directed acyclic graphs (DAGs) are increasingly used across disciplines for causal inference.
• Their application remains limited in behavioral ecology and evolution despite complex variable relationships in field studies.

Purpose of the Study:

• To highlight the value of DAGs in behavioral ecology and evolution.
• To demonstrate how DAGs clarify assumptions and improve statistical modeling.

Main Methods:

• The study uses concrete examples to illustrate DAG application.
• It emphasizes DAGs' role in identifying "bad controls" and structuring variable relationships.

Main Results:

• DAGs clarify implicit assumptions in empirical studies.
• They aid researchers in explicitly considering variable relationships for statistical models.
• DAGs facilitate more rigorous and reliable peer review and meta-analysis.

Conclusions:

• DAGs enhance transparency and understanding in behavioral ecology research.
• Increased adoption of DAGs can improve study reproducibility, reliability, and replicability.