Transporting Causal Effects in Ecology: Concepts, Models and Software

Abstract

O_LIStatistical methods related to causal inference are fundamental in ecological research as ecologists often deal with causal research questions. Consequently, recent years have seen an increase in articles discussing causal inference in ecological context. However, generalizing causal findings across ecological systems that differ in environmental context still remains a challenge. While we may assess causal relationships in one location or population from experimental or observational data, replicating these findings in different settings can be impractical, expensive, or sometimes impossible. C_LIO_LIWe introduce causal effect transportability to ecological research - a formal framework for transferring causal effects assessed in one domain (the source) to estimate outcomes in different domain (the target), where broader data collection may be infeasible. Using structural causal models, this framework provides formal criteria for determining when causal effects can be validly transferred between populations and derives appropriate statistical adjustment formulas when the transportation is possible. Recent algorithmic developments, implemented in accessible R software packages, automate the mathematical derivations and make transportability analysis more practical for ecologists. C_LIO_LIWe demonstrate the framework through a case study examining the effect of tree canopy cover on dissolved oxygen concentrations across different watersheds. We succeed to show that transported estimates outperform naive applications of source population models. C_LIO_LICausal effect transportability offers critical tools for predicting ecological responses across heterogeneous settings, with particular relevance when experimental replication is constrained by cost, ethics, or urgency, and when management decisions require extrapolating findings to novel environmental contexts. C_LI