OOS 38: Causal Inference in Global Change Studies: New Approaches and Emerging Opportunities

Session Organizer(s)

• Joan Dudney

  Department of Plant Sciences, University of California, Davis
  Davis, CA, USA

Moderator(s)

• Laura Dee

  University of Colorado-Boulder
  Boulder, CO, USA

Volunteer(s)

• Aanuoluwapo Olajide

The increasing demand for ecologists to describe and understand the unprecedented global changes transpiring in our study systems has highlighted the limits of our current experimental and analytical approaches. Often the scale at which these events are occurring are beyond the scope of field and lab experiments, and it is difficult to parse the relative importance of one driver over another using widely applied statistical approaches. With rapidly advancing remotely sensed data and increasingly available long-term ecological data, new opportunities are emerging for ecologist to measure local and landscape-scale drivers of global change. Harnessing these data and developing rigorous analyses that enable a greater causal understanding of ecological change, however, remains and outstanding challenge. In this session, we will highlight cutting-edge approaches that leverage observational, timeseries data to infer casual links between outcomes and drivers of global change. Specifically, we will present six studies spanning multiple systems, including forests, grasslands, wildfire, and infectious disease; these studies also span a variety of spatial scales—from field experiments to global observational studies. Each presentation will highlight specific methods that can help overcome bias, which often precludes causal inference in ecological analysis. These methods include difference-in-difference, within-estimator panel data models, instrumental variables, and propensity score matching. Ultimately, we aim to provide accessible examples of causal inference approaches from other disciplines, like economics and public health, that can be applied to a diversity of ecological systems and hypothesis testing. These approaches greatly enhance our abilities as ecologists to harness the data revolution.

Presentations:

• ON DEMAND

  Causal inference in ecological experiments
  

  Presenting Author: Kaitlin Kimmel – Department of Earth & Planetary Sciences, Johns Hopkins Univeristy
• ON DEMAND

  Cutting to the core: Measuring variable drought sensitivity in tree species across their climatic niches
  

  Presenting Author: Robert Heilmayr – Environmental Studies Program, University of California, Santa Barbara
• ON DEMAND

  OOPS! I forgot to measure that! Coping with omitted variable bias for the causal analysis of observational data
  

  Presenting Author: Jarrett E.K. Byrnes
• ON DEMAND

  Prediction and causal inference in ecology
  

  Presenting Author: Paul Ferraro – Carey Business School and Department of Environmental Health and Engineering, Johns Hopkins University
• ON DEMAND

  Quantifying drivers of change in social-ecological systems: Land management impacts wildfire probability in the western US
  

  Presenting Author: Katherine J. Siegel – Department of Environmental Science, Policy, and Management, UC Berkeley
• ON DEMAND

  Using causal inference to detect a climate change signal on infectious tree disease
  

  Presenting Author: Joan Dudney – Department of Plant Sciences, University of California, Davis