Estimating population change as heterogenous treatment effects with citizen science data

Oregon Convention Center 

The increasing volumes of species observation data being collected by citizen-science projects around the world have great potential for monitoring populations and helping to identify the drivers of population change. However, to realize this potential requires methods that can 1) estimate heterogenous patterns of population change that arise when multiple drivers (e.g. change in land use and climate) affect species populations simultaneously, and 2) control for confounding sources of inter-annual variation common in citizen science datasets. In this presentation we investigate the use of machine learning-based estimators designed for Conditional Average Treatment Effect (CATE) estimation (including Causal Forests and meta-learners) to address these challenges. Using a simulation study and data from the citizen-science project eBird, we assess performance estimating spatially varying trends in population size and identifying drivers of population change in the face of real-world confounding. We discuss results showing how this approach can recover heterogenous trends and discuss outstanding challenges.

Biodiversity monitoring, Conservation, Ecology, Causal machine learning, Double machine learning, Spatiotemporal, Species distribution modelling 

Section on Statistics and the Environment