Matching biodiversity indicators to policy needs

Policy makers rely on biodiversity indicators to assess when, where and how nature is changing. Some indicators, however, respond more quickly to pressures than others, measuring short‐term and potentially reversible change, while others capture permanent loss of biodiversity. These characteristics influence an indicator's suitability to perform predictive versus retrospective evaluation functions, but are rarely considered when developing or interpreting indicators. We demonstrate how a conceptual model from economics can be adapted for biodiversity to classify indicators by three functions: Leading (indicators that change prior the subject of interest, thus informing preventative measures); Coincident (indicators that measure the subject of interest); or Lagging (indicators that signal change after the subject of interest, used to evaluate past actions). We use this approach to classify existing indicators for two case studies: global species extinction and marine ecosystem collapse. We find that many existing indicators are theoretically capable of performing different functions; data analysis will be required to confirm temporal relationships between indicators. Classifying indicators according to function enhances interpretability, supports preventative action and facilitates structured decision‐making.