Projecting the performance of conservation interventions

Successful decision-making for environmental management requires evidence of the performance and efficacy of
proposed conservation interventions. Projecting the future impacts of prospective conservation policies and
programs is challenging due to a range of complex ecological, economic, social and ethical factors, and in
particular the need to extrapolate models to novel contexts. Yet many extrapolation techniques currently employed
are limited by unfounded assumptions of causality and a reliance on potentially biased inferences drawn
from limited data. We show how these restrictions can be overcome by established and emerging techniques
from causal inference, scenario analysis, systematic review, expert elicitation, and global sensitivity analysis.
These technical advances provide avenues to untangle cause from correlation, evaluate and transfer models
between contexts, characterize uncertainty, and address imperfect data. With more rigorous projections of
prospective performance of interventions, scientists can deliver policy and program advice that is more scientifically
credible.