Best practices for moving from correlation to causation in ecological research
In ecology, causal questions are ubiquitous, yet the literature describing systematic approaches to answering these questions is vast and fragmented across different traditions (e.g., randomization, structural equation modeling, convergent cross mapping). In our Perspective, we connect the causal assumptions, tasks, frameworks, and methods across these traditions, thereby providing a synthesis of the concepts and methodological advances for detecting and quantifying causal relationships in ecological systems. Through a newly developed workflow, we emphasize how ecologists' choices among empirical approaches are guided by the pre-existing knowledge that ecologists have and the causal assumptions that ecologists are willing to make.
Institutions
- University of Minnesota (US)
- Minnesota Department of Natural Resources (US)
- Université de Sherbrooke (CA)
- Johns Hopkins University (US)
- Scripps Institution of Oceanography (US)
- Cooperative Institute for Research in Environmental Sciences (US)
- University of Potsdam (DE)
- University of Toronto (CA)
- University of Colorado Boulder (US)
- George Washington University (US)
- University of North Dakota (US)
- Johns Hopkins Medicine (US)
- University of Massachusetts Boston (US)
- University of California San Diego (US)
- Technische Universität Berlin (DE)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-02-23
- DOI
- https://doi.org/10.1038/s41467-026-69878-z
- Citations
- 2
- Primary Topic
- Sustainability and Ecological Systems Analysis
- Type
- article
- Field-Weighted Citation Impact
- 21.83
Funders
- U.S. Department of Agriculture
- Johns Hopkins University
- National Institute of Food and Agriculture