Measuring at all scales: sourcing data for more flexible restoration references

Restoration has long used the reference concept as a cornerstone in setting targets, designing interventions, and benchmarking success. Following the initial applications of restoration references, however, the definition and broader relevance has been debated. Particularly in an era of directional global change, using historic or even contemporary ecosystem models has been contentious among restoration scientists and practitioners. In response, there have been calls for increasing flexibility in how references are defined and diversifying sources of data used to describe a reference. Previous frameworks suggest reference information can be drawn from sources across two main axes of time and space, covering historic to contemporary sources, and near to far spatial scales. We extend these axes by including future projections of climate and species composition and regional ecological information that is spatially disconnected from defined ecosystem types. Using this new framework, we conducted a review of restoration literature published between 2010 and 2020, extracting the temporal and spatial scales of reference data and classifying reference metrics by data type. The studies overwhelmingly focused on contemporary, ecosystem-specific references to benchmark a completed project's success. The most commonly reported reference metrics were plant-based, and contemporary reference data sources were more diverse than historical or future reference data. As global conditions continue to shift, we suggest that restoration projects would benefit by expanding reference site information to include forecasted and spatially diverse data. A greater diversity of data sources can enable higher flexibility and long-term restoration success in the face of global change.

Automated summary

The use of contemporary, ecosystem-specific reference data has been common in restoration projects, but as global conditions continue to shift, there is a call for greater diversity in data sources, including forecasted and spatially diverse information, to enhance flexibility and long-term success in restoration efforts.